!build-info.cpp.in
!build-info.sh
!build.zig
+!docs/build.md
/libllama.so
/llama-*
android-ndk-*
+++ /dev/null
-# llama.cpp for SYCL
-
-- [Background](#background)
-- [Recommended Release](#recommended-release)
-- [News](#news)
-- [OS](#os)
-- [Hardware](#hardware)
-- [Docker](#docker)
-- [Linux](#linux)
-- [Windows](#windows)
-- [Environment Variable](#environment-variable)
-- [Known Issue](#known-issues)
-- [Q&A](#qa)
-- [TODO](#todo)
-
-## Background
-
-**SYCL** is a high-level parallel programming model designed to improve developers productivity writing code across various hardware accelerators such as CPUs, GPUs, and FPGAs. It is a single-source language designed for heterogeneous computing and based on standard C++17.
-
-**oneAPI** is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include:
-
-- **DPCPP** *(Data Parallel C++)*: The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers.
-- **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. oneMKL - Math Kernel Library)*.
-- **oneAPI LevelZero**: A high performance low level interface for fine-grained control over intel iGPUs and dGPUs.
-- **Nvidia & AMD Plugins**: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets.
-
-### Llama.cpp + SYCL
-
-The llama.cpp SYCL backend is designed to support **Intel GPU** firstly. Based on the cross-platform feature of SYCL, it could support other vendor GPUs: Nvidia GPU (*AMD GPU coming*).
-
-When targeting **Intel CPU**, it is recommended to use llama.cpp for [Intel oneMKL](README.md#intel-onemkl) backend.
-
-It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose.
-
-## Recommended Release
-
-The SYCL backend would be broken by some PRs due to no online CI.
-
-The following release is verified with good quality:
-
-|Commit ID|Tag|Release|Verified Platform|
-|-|-|-|-|
-|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggerganov/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1|
-
-
-## News
-
-- 2024.5
- - Performance is increased: 34 -> 37 tokens/s of llama-2-7b.Q4_0 on Arc770.
- - Arch Linux is verified successfully.
-
-- 2024.4
- - Support data types: GGML_TYPE_IQ4_NL, GGML_TYPE_IQ4_XS, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ3_S, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M.
-
-- 2024.3
- - Release binary files of Windows.
- - A blog is published: **Run LLM on all Intel GPUs Using llama.cpp**: [intel.com](https://www.intel.com/content/www/us/en/developer/articles/technical/run-llm-on-all-gpus-using-llama-cpp-artical.html) or [medium.com](https://medium.com/@jianyu_neo/run-llm-on-all-intel-gpus-using-llama-cpp-fd2e2dcbd9bd).
- - New base line is ready: [tag b2437](https://github.com/ggerganov/llama.cpp/tree/b2437).
- - Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing.
- - Support to assign main GPU by **--main-gpu**, replace $GGML_SYCL_DEVICE.
- - Support detecting all GPUs with level-zero and same top **Max compute units**.
- - Support OPs
- - hardsigmoid
- - hardswish
- - pool2d
-
-- 2024.1
- - Create SYCL backend for Intel GPU.
- - Support Windows build
-
-## OS
-
-| OS | Status | Verified |
-|---------|---------|------------------------------------------------|
-| Linux | Support | Ubuntu 22.04, Fedora Silverblue 39, Arch Linux |
-| Windows | Support | Windows 11 |
-
-
-## Hardware
-
-### Intel GPU
-
-**Verified devices**
-
-| Intel GPU | Status | Verified Model |
-|-------------------------------|---------|---------------------------------------|
-| Intel Data Center Max Series | Support | Max 1550, 1100 |
-| Intel Data Center Flex Series | Support | Flex 170 |
-| Intel Arc Series | Support | Arc 770, 730M, Arc A750 |
-| Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake |
-| Intel iGPU | Support | iGPU in i5-1250P, i7-1260P, i7-1165G7 |
-
-*Notes:*
-
-- **Memory**
- - The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/llama-cli`.
-
- - Please make sure the GPU shared memory from the host is large enough to account for the model's size. For e.g. the *llama-2-7b.Q4_0* requires at least 8.0GB for integrated GPU and 4.0GB for discrete GPU.
-
-- **Execution Unit (EU)**
- - If the iGPU has less than 80 EUs, the inference speed will likely be too slow for practical use.
-
-### Other Vendor GPU
-
-**Verified devices**
-
-| Nvidia GPU | Status | Verified Model |
-|--------------------------|---------|----------------|
-| Ampere Series | Support | A100, A4000 |
-| Ampere Series *(Mobile)* | Support | RTX 40 Series |
-
-## Docker
-The docker build option is currently limited to *intel GPU* targets.
-
-### Build image
-```sh
-# Using FP16
-docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" -f .devops/llama-cli-intel.Dockerfile .
-```
-
-*Notes*:
-
-To build in default FP32 *(Slower than FP16 alternative)*, you can remove the `--build-arg="GGML_SYCL_F16=ON"` argument from the previous command.
-
-You can also use the `.devops/llama-server-intel.Dockerfile`, which builds the *"server"* alternative.
-
-### Run container
-
-```sh
-# First, find all the DRI cards
-ls -la /dev/dri
-# Then, pick the card that you want to use (here for e.g. /dev/dri/card1).
-docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
-```
-
-*Notes:*
-- Docker has been tested successfully on native Linux. WSL support has not been verified yet.
-- You may need to install Intel GPU driver on the **host** machine *(Please refer to the [Linux configuration](#linux) for details)*.
-
-## Linux
-
-### I. Setup Environment
-
-1. **Install GPU drivers**
-
- - **Intel GPU**
-
-Intel data center GPUs drivers installation guide and download page can be found here: [Get intel dGPU Drivers](https://dgpu-docs.intel.com/driver/installation.html#ubuntu-install-steps).
-
-*Note*: for client GPUs *(iGPU & Arc A-Series)*, please refer to the [client iGPU driver installation](https://dgpu-docs.intel.com/driver/client/overview.html).
-
-Once installed, add the user(s) to the `video` and `render` groups.
-
-```sh
-sudo usermod -aG render $USER
-sudo usermod -aG video $USER
-```
-
-*Note*: logout/re-login for the changes to take effect.
-
-Verify installation through `clinfo`:
-
-```sh
-sudo apt install clinfo
-sudo clinfo -l
-```
-
-Sample output:
-
-```sh
-Platform #0: Intel(R) OpenCL Graphics
- `-- Device #0: Intel(R) Arc(TM) A770 Graphics
-
-Platform #0: Intel(R) OpenCL HD Graphics
- `-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49]
-```
-
-- **Nvidia GPU**
-
-In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements *-found [here](README.md#cuda)-* are installed.
-
-2. **Install Intel® oneAPI Base toolkit**
-
-- **For Intel GPU**
-
-The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.
-
-Please follow the instructions for downloading and installing the Toolkit for Linux, and preferably keep the default installation values unchanged, notably the installation path *(`/opt/intel/oneapi` by default)*.
-
-Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.
-
-Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI MKL for intel GPUs.
-
-- **Adding support to Nvidia GPUs**
-
-**oneAPI Plugin**: In order to enable SYCL support on Nvidia GPUs, please install the [Codeplay oneAPI Plugin for Nvidia GPUs](https://developer.codeplay.com/products/oneapi/nvidia/download). User should also make sure the plugin version matches the installed base toolkit one *(previous step)* for a seamless "oneAPI on Nvidia GPU" setup.
-
-
-**oneMKL for cuBlas**: The current oneMKL releases *(shipped with the oneAPI base-toolkit)* do not contain the cuBLAS backend. A build from source of the upstream [oneMKL](https://github.com/oneapi-src/oneMKL) with the *cuBLAS* backend enabled is thus required to run it on Nvidia GPUs.
-
-```sh
-git clone https://github.com/oneapi-src/oneMKL
-cd oneMKL
-cmake -B buildWithCublas -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON -DTARGET_DOMAINS=blas
-cmake --build buildWithCublas --config Release
-```
-
-
-3. **Verify installation and environment**
-
-In order to check the available SYCL devices on the machine, please use the `sycl-ls` command.
-```sh
-source /opt/intel/oneapi/setvars.sh
-sycl-ls
-```
-
-- **Intel GPU**
-
-When targeting an intel GPU, the user should expect one or more level-zero devices among the available SYCL devices. Please make sure that at least one GPU is present, for instance [`ext_oneapi_level_zero:gpu:0`] in the sample output below:
-
-```
-[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000]
-[opencl:cpu:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i7-13700K OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
-[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO [23.30.26918.50]
-[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26918]
-```
-
-- **Nvidia GPU**
-
-Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA device [`ext_oneapi_cuda:gpu`] as bellow:
-```
-[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.12.0.12_195853.xmain-hotfix]
-[opencl:cpu:1] Intel(R) OpenCL, Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz OpenCL 3.0 (Build 0) [2023.16.12.0.12_195853.xmain-hotfix]
-[ext_oneapi_cuda:gpu:0] NVIDIA CUDA BACKEND, NVIDIA A100-PCIE-40GB 8.0 [CUDA 12.2]
-```
-
-### II. Build llama.cpp
-
-#### Intel GPU
-```sh
-# Export relevant ENV variables
-source /opt/intel/oneapi/setvars.sh
-
-# Build LLAMA with MKL BLAS acceleration for intel GPU
-
-# Option 1: Use FP32 (recommended for better performance in most cases)
-cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
-
-# Option 2: Use FP16
-cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON
-
-# build all binary
-cmake --build build --config Release -j -v
-```
-
-#### Nvidia GPU
-```sh
-# Export relevant ENV variables
-export LD_LIBRARY_PATH=/path/to/oneMKL/buildWithCublas/lib:$LD_LIBRARY_PATH
-export LIBRARY_PATH=/path/to/oneMKL/buildWithCublas/lib:$LIBRARY_PATH
-export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithCublas/include:$CPLUS_INCLUDE_DIR
-export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR
-
-# Build LLAMA with Nvidia BLAS acceleration through SYCL
-
-# Option 1: Use FP32 (recommended for better performance in most cases)
-cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
-
-# Option 2: Use FP16
-cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON
-
-# build all binary
-cmake --build build --config Release -j -v
-
-```
-
-### III. Run the inference
-
-1. Retrieve and prepare model
-
-You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
-
-2. Enable oneAPI running environment
-
-```sh
-source /opt/intel/oneapi/setvars.sh
-```
-
-3. List devices information
-
-Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:
-
-```sh
-./build/bin/llama-ls-sycl-device
-```
-A example of such log in a system with 1 *intel CPU* and 1 *intel GPU* can look like the following:
-```
-found 6 SYCL devices:
-| | | |Compute |Max compute|Max work|Max sub| |
-|ID| Device Type| Name|capability|units |group |group |Global mem size|
-|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
-| 0|[level_zero:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 1.3| 512| 1024| 32| 16225243136|
-| 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216|
-| 2| [opencl:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 3.0| 512| 1024| 32| 16225243136|
-| 3| [opencl:gpu:1]| Intel(R) UHD Graphics 770| 3.0| 32| 512| 32| 53651849216|
-| 4| [opencl:cpu:0]| 13th Gen Intel(R) Core(TM) i7-13700K| 3.0| 24| 8192| 64| 67064815616|
-| 5| [opencl:acc:0]| Intel(R) FPGA Emulation Device| 1.2| 24|67108864| 64| 67064815616|
-```
-
-| Attribute | Note |
-|------------------------|-------------------------------------------------------------|
-| compute capability 1.3 | Level-zero driver/runtime, recommended |
-| compute capability 3.0 | OpenCL driver/runtime, slower than level-zero in most cases |
-
-4. Launch inference
-
-There are two device selection modes:
-
-- Single device: Use one device target specified by the user.
-- Multiple devices: Automatically select the devices with the same largest Max compute-units.
-
-| Device selection | Parameter |
-|------------------|----------------------------------------|
-| Single device | --split-mode none --main-gpu DEVICE_ID |
-| Multiple devices | --split-mode layer (default) |
-
-Examples:
-
-- Use device 0:
-
-```sh
-ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0
-```
-or run by script:
-
-```sh
-./examples/sycl/run_llama2.sh 0
-```
-
-- Use multiple devices:
-
-```sh
-ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer
-```
-
-Otherwise, you can run the script:
-
-```sh
-./examples/sycl/run_llama2.sh
-```
-
-*Notes:*
-
-- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:
-
-```sh
-detect 1 SYCL GPUs: [0] with top Max compute units:512
-```
-Or
-```sh
-use 1 SYCL GPUs: [0] with Max compute units:512
-```
-
-## Windows
-
-### I. Setup Environment
-
-1. Install GPU driver
-
-Intel GPU drivers instructions guide and download page can be found here: [Get intel GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html).
-
-2. Install Visual Studio
-
-If you already have a recent version of Microsoft Visual Studio, you can skip this step. Otherwise, please refer to the official download page for [Microsoft Visual Studio](https://visualstudio.microsoft.com/).
-
-3. Install Intel® oneAPI Base toolkit
-
-The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.
-
-Please follow the instructions for downloading and installing the Toolkit for Windows, and preferably keep the default installation values unchanged, notably the installation path *(`C:\Program Files (x86)\Intel\oneAPI` by default)*.
-
-Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.
-
-b. Enable oneAPI running environment:
-
-- Type "oneAPI" in the search bar, then open the `Intel oneAPI command prompt for Intel 64 for Visual Studio 2022` App.
-
-- On the command prompt, enable the runtime environment with the following:
-```
-"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
-```
-
-c. Verify installation
-
-In the oneAPI command line, run the following to print the available SYCL devices:
-
-```
-sycl-ls
-```
-
-There should be one or more *level-zero* GPU devices displayed as **[ext_oneapi_level_zero:gpu]**. Below is example of such output detecting an *intel Iris Xe* GPU as a Level-zero SYCL device:
-
-Output (example):
-```
-[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000]
-[opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
-[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO [31.0.101.5186]
-[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044]
-```
-
-4. Install build tools
-
-a. Download & install cmake for Windows: https://cmake.org/download/ (CMake can also be installed from Visual Studio Installer)
-b. The new Visual Studio will install Ninja as default. (If not, please install it manually: https://ninja-build.org/)
-
-
-### II. Build llama.cpp
-
-On the oneAPI command line window, step into the llama.cpp main directory and run the following:
-
-```
-@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
-
-# Option 1: Use FP32 (recommended for better performance in most cases)
-cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release
-
-# Option 2: Or FP16
-cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DGGML_SYCL_F16=ON
-
-cmake --build build --config Release -j
-```
-
-Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions:
-```sh
-.\examples\sycl\win-build-sycl.bat
-```
-
-Or, use CMake presets to build:
-```sh
-cmake --preset x64-windows-sycl-release
-cmake --build build-x64-windows-sycl-release -j --target llama-cli
-
-cmake -DGGML_SYCL_F16=ON --preset x64-windows-sycl-release
-cmake --build build-x64-windows-sycl-release -j --target llama-cli
-
-cmake --preset x64-windows-sycl-debug
-cmake --build build-x64-windows-sycl-debug -j --target llama-cli
-```
-
-Or, you can use Visual Studio to open llama.cpp folder as a CMake project. Choose the sycl CMake presets (`x64-windows-sycl-release` or `x64-windows-sycl-debug`) before you compile the project.
-
-*Notes:*
-
-- In case of a minimal experimental setup, the user can build the inference executable only through `cmake --build build --config Release -j --target llama-cli`.
-
-### III. Run the inference
-
-1. Retrieve and prepare model
-
-You can refer to the general [*Prepare and Quantize*](README#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
-
-2. Enable oneAPI running environment
-
-On the oneAPI command line window, run the following and step into the llama.cpp directory:
-```
-"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
-```
-
-3. List devices information
-
-Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:
-
-```
-build\bin\ls-sycl-device.exe
-```
-
-The output of this command in a system with 1 *intel CPU* and 1 *intel GPU* would look like the following:
-```
-found 6 SYCL devices:
-| | | |Compute |Max compute|Max work|Max sub| |
-|ID| Device Type| Name|capability|units |group |group |Global mem size|
-|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
-| 0|[level_zero:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 1.3| 512| 1024| 32| 16225243136|
-| 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216|
-| 2| [opencl:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 3.0| 512| 1024| 32| 16225243136|
-| 3| [opencl:gpu:1]| Intel(R) UHD Graphics 770| 3.0| 32| 512| 32| 53651849216|
-| 4| [opencl:cpu:0]| 13th Gen Intel(R) Core(TM) i7-13700K| 3.0| 24| 8192| 64| 67064815616|
-| 5| [opencl:acc:0]| Intel(R) FPGA Emulation Device| 1.2| 24|67108864| 64| 67064815616|
-
-```
-
-| Attribute | Note |
-|------------------------|-----------------------------------------------------------|
-| compute capability 1.3 | Level-zero running time, recommended |
-| compute capability 3.0 | OpenCL running time, slower than level-zero in most cases |
-
-
-4. Launch inference
-
-There are two device selection modes:
-
-- Single device: Use one device assigned by user.
-- Multiple devices: Automatically choose the devices with the same biggest Max compute units.
-
-| Device selection | Parameter |
-|------------------|----------------------------------------|
-| Single device | --split-mode none --main-gpu DEVICE_ID |
-| Multiple devices | --split-mode layer (default) |
-
-Examples:
-
-- Use device 0:
-
-```
-build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm none -mg 0
-```
-
-- Use multiple devices:
-
-```
-build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm layer
-```
-Otherwise, run the following wrapper script:
-
-```
-.\examples\sycl\win-run-llama2.bat
-```
-
-Note:
-
-- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:
-
-```sh
-detect 1 SYCL GPUs: [0] with top Max compute units:512
-```
-Or
-```sh
-use 1 SYCL GPUs: [0] with Max compute units:512
-```
-
-## Environment Variable
-
-#### Build
-
-| Name | Value | Function |
-|--------------------|-----------------------------------|---------------------------------------------|
-| GGML_SYCL | ON (mandatory) | Enable build with SYCL code path. |
-| GGML_SYCL_TARGET | INTEL *(default)* \| NVIDIA | Set the SYCL target device type. |
-| GGML_SYCL_F16 | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path. |
-| CMAKE_C_COMPILER | icx | Set *icx* compiler for SYCL code path. |
-| CMAKE_CXX_COMPILER | icpx *(Linux)*, icx *(Windows)* | Set `icpx/icx` compiler for SYCL code path. |
-
-#### Runtime
-
-| Name | Value | Function |
-|-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------|
-| GGML_SYCL_DEBUG | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG |
-| ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
-
-## Known Issues
-
-- `Split-mode:[row]` is not supported.
-
-## Q&A
-
-- Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`.
-
- - Potential cause: Unavailable oneAPI installation or not set ENV variables.
- - Solution: Install *oneAPI base toolkit* and enable its ENV through: `source /opt/intel/oneapi/setvars.sh`.
-
-- General compiler error:
-
- - Remove **build** folder or try a clean-build.
-
-- I can **not** see `[ext_oneapi_level_zero:gpu]` afer installing the GPU driver on Linux.
-
- Please double-check with `sudo sycl-ls`.
-
- If it's present in the list, please add video/render group to your user then **logout/login** or restart your system:
-
- ```
- sudo usermod -aG render $USER
- sudo usermod -aG video $USER
- ```
- Otherwise, please double-check the GPU driver installation steps.
-
-### **GitHub contribution**:
-Please add the **[SYCL]** prefix/tag in issues/PRs titles to help the SYCL-team check/address them without delay.
-
-## TODO
-
-- Support row layer split for multiple card runs.
> [!IMPORTANT]
[2024 Jun 12] Binaries have been renamed w/ a `llama-` prefix. `main` is now `llama-cli`, `server` is `llama-server`, etc (https://github.com/ggerganov/llama.cpp/pull/7809)
-### Recent API changes
+## Recent API changes
- [2024 Jun 26] The source code and CMake build scripts have been restructured https://github.com/ggerganov/llama.cpp/pull/8006
- [2024 Apr 21] `llama_token_to_piece` can now optionally render special tokens https://github.com/ggerganov/llama.cpp/pull/6807
- [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796
- [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849
-### Hot topics
+## Hot topics
- **`convert.py` has been deprecated and moved to `examples/convert_legacy_llama.py`, please use `convert_hf_to_gguf.py`** https://github.com/ggerganov/llama.cpp/pull/7430
- Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021
----
-<details>
- <summary>Table of Contents</summary>
- <ol>
- <li>
- <a href="#description">Description</a>
- </li>
- <li>
- <a href="#usage">Usage</a>
- <ul>
- <li><a href="#get-the-code">Get the Code</a></li>
- <li><a href="#build">Build</a></li>
- <li><a href="#blas-build">BLAS Build</a></li>
- <li><a href="#prepare-and-quantize">Prepare and Quantize</a></li>
- <li><a href="#run-the-quantized-model">Run the quantized model</a></li>
- <li><a href="#memorydisk-requirements">Memory/Disk Requirements</a></li>
- <li><a href="#quantization">Quantization</a></li>
- <li><a href="#interactive-mode">Interactive mode</a></li>
- <li><a href="#constrained-output-with-grammars">Constrained output with grammars</a></li>
- <li><a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a></li>
- <li><a href="#seminal-papers-and-background-on-the-models">Seminal papers and background on the models</a></li>
- <li><a href="#perplexity-measuring-model-quality">Perplexity (measuring model quality)</a></li>
- <li><a href="#android">Android</a></li>
- <li><a href="#docker">Docker</a></li>
- </ul>
- </li>
- <li><a href="#contributing">Contributing</a></li>
- <li><a href="#coding-guidelines">Coding guidelines</a></li>
- <li><a href="#docs">Docs</a></li>
- </ol>
-</details>
-
## Description
The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide
improved significantly thanks to many contributions. It is the main playground for developing new features for the
[ggml](https://github.com/ggerganov/ggml) library.
-**Supported platforms:**
-
-- [X] Mac OS
-- [X] Linux
-- [X] Windows (via CMake)
-- [X] Docker
-- [X] FreeBSD
-
**Supported models:**
Typically finetunes of the base models below are supported as well.
- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
- [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
-**HTTP server**
-
-[llama.cpp web server](./examples/server) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients.
-
-[simplechat](./examples/server/public_simplechat) is a simple chat client, which can be used to chat with the model exposed using above web server (use --path to point to simplechat), from a local web browser.
-
**Bindings:**
- Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
- [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp
----
+## Demo
-Here is a typical run using LLaMA v2 13B on M2 Ultra:
+<details>
+<summary>Typical run using LLaMA v2 13B on M2 Ultra</summary>
```
$ make -j && ./llama-cli -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
llama_print_timings: total time = 25431.49 ms
```
+</details>
+
+<details>
+<summary>Demo of running both LLaMA-7B and whisper.cpp on a single M1 Pro MacBook</summary>
+
And here is another demo of running both LLaMA-7B and [whisper.cpp](https://github.com/ggerganov/whisper.cpp) on a single M1 Pro MacBook:
https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8b4f-add84093ffff.mp4
+</details>
+
## Usage
Here are the end-to-end binary build and model conversion steps for most supported models.
-### Get the Code
-
-```bash
-git clone https://github.com/ggerganov/llama.cpp
-cd llama.cpp
-```
-
-### Build
-
-In order to build llama.cpp you have four different options.
-
-- Using `make`:
- - On Linux or MacOS:
-
- ```bash
- make
- ```
+### Basic usage
- - On Windows:
+Firstly, you need to get the binary. There are different methods that you can follow:
+- Method 1: Clone this repository and build locally, see [how to build](./docs/build.md)
+- Method 2: If you are using MacOS or Linux, you can install llama.cpp via [brew, flox or nix](./docs/install.md)
+- Method 3: Use a Docker image, see [documentation for Docker](./docs/docker.md)
+- Method 4: Download pre-built binary from [releases](https://github.com/ggerganov/llama.cpp/releases)
- 1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
- 2. Extract `w64devkit` on your pc.
- 3. Run `w64devkit.exe`.
- 4. Use the `cd` command to reach the `llama.cpp` folder.
- 5. From here you can run:
- ```bash
- make
- ```
+You can run a basic completion using this command:
- - Notes:
- - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `make -j 8` will run 8 jobs in parallel.
- - For faster repeated compilation, install [ccache](https://ccache.dev/).
- - For debug builds, run `make LLAMA_DEBUG=1`
-
-- Using `CMake`:
-
- ```bash
- cmake -B build
- cmake --build build --config Release
- ```
-
- **Notes**:
-
- - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel.
- - For faster repeated compilation, install [ccache](https://ccache.dev/).
- - For debug builds, there are two cases:
-
- 1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag):
-
- ```bash
- cmake -B build -DCMAKE_BUILD_TYPE=Debug
- cmake --build build
- ```
-
- 2. Multi-config generators (`-G` param set to Visual Studio, XCode...):
-
- ```bash
- cmake -B build -G "Xcode"
- cmake --build build --config Debug
- ```
-
-- Using `gmake` (FreeBSD):
-
- 1. Install and activate [DRM in FreeBSD](https://wiki.freebsd.org/Graphics)
- 2. Add your user to **video** group
- 3. Install compilation dependencies.
-
- ```bash
- sudo pkg install gmake automake autoconf pkgconf llvm15 openblas
-
- gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4
- ```
-
-### Homebrew
+```bash
+llama-cli -m your_model.gguf -p "I believe the meaning of life is" -n 128
-On Mac and Linux, the homebrew package manager can be used via
+# Output:
+# I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey.
```
-brew install llama.cpp
-```
-The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggerganov/llama.cpp/discussions/7668
-
-### Nix
-On Mac and Linux, the Nix package manager can be used via
-```
-nix profile install nixpkgs#llama-cpp
-```
-For flake enabled installs.
+See [this page](./examples/main/README.md) for a full list of parameters.
-Or
-```
-nix-env --file '<nixpkgs>' --install --attr llama-cpp
-```
-For non-flake enabled installs.
+### Conversation mode
-This expression is automatically updated within the [nixpkgs repo](https://github.com/NixOS/nixpkgs/blob/nixos-24.05/pkgs/by-name/ll/llama-cpp/package.nix#L164).
+If you want a more ChatGPT-like experience, you can run in conversation mode by passing `-cnv` as a parameter:
-#### Flox
+```bash
+llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv
-On Mac and Linux, Flox can be used to install llama.cpp within a Flox environment via
+# Output:
+# > hi, who are you?
+# Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today?
+#
+# > what is 1+1?
+# Easy peasy! The answer to 1+1 is... 2!
```
-flox install llama-cpp
-```
-Flox follows the nixpkgs build of llama.cpp.
-
-### Metal Build
-
-On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
-To disable the Metal build at compile time use the `GGML_NO_METAL=1` flag or the `GGML_METAL=OFF` cmake option.
-
-When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers|-ngl 0` command-line
-argument.
-
-### BLAS Build
-
-Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS. There are currently several different BLAS implementations available for build and use:
-
-- #### Accelerate Framework:
-
- This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.
-
-- #### OpenBLAS:
-
- This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.
-
- - Using `make`:
- - On Linux:
- ```bash
- make GGML_OPENBLAS=1
- ```
-
- - On Windows:
-
- 1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
- 2. Download the latest version of [OpenBLAS for Windows](https://github.com/xianyi/OpenBLAS/releases).
- 3. Extract `w64devkit` on your pc.
- 4. From the OpenBLAS zip that you just downloaded copy `libopenblas.a`, located inside the `lib` folder, inside `w64devkit\x86_64-w64-mingw32\lib`.
- 5. From the same OpenBLAS zip copy the content of the `include` folder inside `w64devkit\x86_64-w64-mingw32\include`.
- 6. Run `w64devkit.exe`.
- 7. Use the `cd` command to reach the `llama.cpp` folder.
- 8. From here you can run:
-
- ```bash
- make GGML_OPENBLAS=1
- ```
-
- - Using `CMake` on Linux:
-
- ```bash
- cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
- cmake --build build --config Release
- ```
-
-- #### BLIS
-
- Check [BLIS.md](docs/BLIS.md) for more information.
-
-- #### SYCL
- SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators.
-
- llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).
-
- For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md).
-
-- #### Intel oneMKL
- Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./README-sycl.md).
-
- - Using manual oneAPI installation:
- By default, `GGML_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DGGML_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:
- ```bash
- source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation
- cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_NATIVE=ON
- cmake --build build --config Release
- ```
-
- - Using oneAPI docker image:
- If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above.
-
- Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information.
-- #### CUDA
-
- This provides GPU acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
-
- For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling.
-
- - Using `make`:
- ```bash
- make GGML_CUDA=1
- ```
- - Using `CMake`:
-
- ```bash
- cmake -B build -DGGML_CUDA=ON
- cmake --build build --config Release
- ```
-
- The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance:
-
- | Option | Legal values | Default | Description |
- |-------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
- | GGML_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
- | GGML_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
- | GGML_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. |
- | GGML_CUDA_FORCE_MMQ | Boolean | false | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, RDNA3). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower. |
- | GGML_CUDA_FORCE_CUBLAS | Boolean | false | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models |
- | GGML_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. |
- | GGML_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
- | GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. |
- | GGML_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. |
-
-- #### hipBLAS
-
- This provides BLAS acceleration on HIP-supported AMD GPUs.
- Make sure to have ROCm installed.
- You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html#rocm-install-quick).
-
- - Using `make`:
- ```bash
- make GGML_HIPBLAS=1
- ```
- - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
- ```bash
- HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
- cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
- && cmake --build build --config Release -- -j 16
- ```
- On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DGGML_HIP_UMA=ON`.
- However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
-
- Note that if you get the following error:
- ```
- clang: error: cannot find ROCm device library; provide its path via '--rocm-path' or '--rocm-device-lib-path', or pass '-nogpulib' to build without ROCm device library
- ```
- Try searching for a directory under `HIP_PATH` that contains the file
- `oclc_abi_version_400.bc`. Then, add the following to the start of the
- command: `HIP_DEVICE_LIB_PATH=<directory-you-just-found>`, so something
- like:
- ```bash
- HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \
- HIP_DEVICE_LIB_PATH=<directory-you-just-found> \
- cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
- && cmake --build build -- -j 16
- ```
-
- - Using `make` (example for target gfx1030, build with 16 CPU threads):
- ```bash
- make -j16 GGML_HIPBLAS=1 GGML_HIP_UMA=1 AMDGPU_TARGETS=gfx1030
- ```
-
- - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU):
- ```bash
- set PATH=%HIP_PATH%\bin;%PATH%
- cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release
- cmake --build build
- ```
- Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors)
- Find your gpu version string by matching the most significant version information from `rocminfo | grep gfx | head -1 | awk '{print $2}'` with the list of processors, e.g. `gfx1035` maps to `gfx1030`.
-
-
- The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
- If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3.
- The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above):
-
- | Option | Legal values | Default | Description |
- |------------------------|------------------------|---------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
- | GGML_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
- | GGML_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
- | GGML_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
-
-- #### Vulkan
-
- **With docker**:
-
- You don't need to install Vulkan SDK. It will be installed inside the container.
-
- ```sh
- # Build the image
- docker build -t llama-cpp-vulkan -f .devops/llama-cli-vulkan.Dockerfile .
-
- # Then, use it:
- docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
- ```
-
- **Without docker**:
-
- Firstly, you need to make sure you have installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html)
-
- For example, on Ubuntu 22.04 (jammy), use the command below:
-
- ```bash
- wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add -
- wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
- apt update -y
- apt-get install -y vulkan-sdk
- # To verify the installation, use the command below:
- vulkaninfo
- ```
-
- Alternatively your package manager might be able to provide the appropriate libraries.
- For example for Ubuntu 22.04 you can install `libvulkan-dev` instead.
- For Fedora 40, you can install `vulkan-devel`, `glslc` and `glslang` packages.
-
- Then, build llama.cpp using the cmake command below:
-
- ```bash
- cmake -B build -DGGML_VULKAN=1
- cmake --build build --config Release
- # Test the output binary (with "-ngl 33" to offload all layers to GPU)
- ./bin/llama-cli -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4
-
- # You should see in the output, ggml_vulkan detected your GPU. For example:
- # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32
- ```
-
-### Prepare and Quantize
-
-> [!NOTE]
-> You can use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to quantise your model weights without any setup too. It is synced from `llama.cpp` main every 6 hours.
-
-To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face.
-
-Note: `convert.py` has been moved to `examples/convert_legacy_llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derivatives.
-It does not support LLaMA 3, you can use `convert_hf_to_gguf.py` with LLaMA 3 downloaded from Hugging Face.
+By default, the chat template will be taken from the input model. If you want to use another chat template, pass `--chat-template NAME` as a parameter. See the list of [supported templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
```bash
-# obtain the official LLaMA model weights and place them in ./models
-ls ./models
-llama-2-7b tokenizer_checklist.chk tokenizer.model
-# [Optional] for models using BPE tokenizers
-ls ./models
-<folder containing weights and tokenizer json> vocab.json
-# [Optional] for PyTorch .bin models like Mistral-7B
-ls ./models
-<folder containing weights and tokenizer json>
-
-# install Python dependencies
-python3 -m pip install -r requirements.txt
-
-# convert the model to ggml FP16 format
-python3 convert_hf_to_gguf.py models/mymodel/
-
-# quantize the model to 4-bits (using Q4_K_M method)
-./llama-quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M
-
-# update the gguf filetype to current version if older version is now unsupported
-./llama-quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY
+./llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv --chat-template chatml
```
-### Run the quantized model
+You can also use your own template via in-prefix, in-suffix and reverse-prompt parameters:
```bash
-# start inference on a gguf model
-./llama-cli -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128
+./llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv --in-prefix 'User: ' --reverse-prompt 'User:'
```
-When running the larger models, make sure you have enough disk space to store all the intermediate files.
-
-### Running on Windows with prebuilt binaries
-
-You will find prebuilt Windows binaries on the release page.
+### Web server
-Simply download and extract the latest zip package of choice: (e.g. `llama-b1380-bin-win-avx2-x64.zip`)
+[llama.cpp web server](./examples/server/README.md) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients.
-From the unzipped folder, open a terminal/cmd window here and place a pre-converted `.gguf` model file. Test out the main example like so:
+Example usage:
-```
-.\main -m llama-2-7b.Q4_0.gguf -n 128
-```
-
-### Memory/Disk Requirements
-
-As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same.
-
-| Model | Original size | Quantized size (Q4_0) |
-|------:|--------------:|----------------------:|
-| 7B | 13 GB | 3.9 GB |
-| 13B | 24 GB | 7.8 GB |
-| 30B | 60 GB | 19.5 GB |
-| 65B | 120 GB | 38.5 GB |
-
-### Quantization
-
-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 |
-| 7B | file size | 13.0G | 3.5G | 3.9G | 4.3G | 4.7G | 6.7G |
-| 7B | ms/tok @ 4th | 127 | 55 | 54 | 76 | 83 | 72 |
-| 7B | ms/tok @ 8th | 122 | 43 | 45 | 52 | 56 | 67 |
-| 7B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 |
-| 13B | perplexity | 5.2543 | 5.3860 | 5.3608 | 5.2856 | 5.2706 | 5.2548 |
-| 13B | file size | 25.0G | 6.8G | 7.6G | 8.3G | 9.1G | 13G |
-| 13B | ms/tok @ 4th | - | 103 | 105 | 148 | 160 | 131 |
-| 13B | ms/tok @ 8th | - | 73 | 82 | 98 | 105 | 128 |
-| 13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 |
-
-- [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684)
-- recent k-quants improvements and new i-quants
- - [#2707](https://github.com/ggerganov/llama.cpp/pull/2707)
- - [#2807](https://github.com/ggerganov/llama.cpp/pull/2807)
- - [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773)
- - [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856)
- - [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861)
- - [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872)
- - [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897)
- - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930)
- - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957)
- - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969)
- - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996)
- - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060)
- - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196)
- - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361)
-
-### Perplexity (measuring model quality)
-
-You can use the `perplexity` example to measure perplexity over a given prompt (lower perplexity is better).
-For more information, see [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity).
-
-The perplexity measurements in table above are done against the `wikitext2` test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512.
-The time per token is measured on a MacBook M1 Pro 32GB RAM using 4 and 8 threads.
-
-#### How to run
+```bash
+./llama-server -m your_model.gguf --port 8080
-1. Download/extract: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
-2. Run `./llama-perplexity -m models/7B/ggml-model-q4_0.gguf -f wiki.test.raw`
-3. Output:
+# Basic web UI can be accessed via browser: http://localhost:8080
+# Chat completion endpoint: http://localhost:8080/v1/chat/completions
```
-perplexity : calculating perplexity over 655 chunks
-24.43 seconds per pass - ETA 4.45 hours
-[1]4.5970,[2]5.1807,[3]6.0382,...
-```
-And after 4.45 hours, you will have the final perplexity.
### Interactive mode
-If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter.
+> [!NOTE]
+> If you prefer basic usage, please consider using conversation mode instead of interactive mode
+
In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMA emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
Here is an example of a few-shot interaction, invoked with the command
- [Aligning language models to follow instructions](https://openai.com/research/instruction-following)
- [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
-### Android
-
-#### Build on Android using Termux
-[Termux](https://github.com/termux/termux-app#installation) is a method to execute `llama.cpp` on an Android device (no root required).
-```
-apt update && apt upgrade -y
-apt install git make cmake
-```
-
-It's recommended to move your model inside the `~/` directory for best performance:
-```
-cd storage/downloads
-mv model.gguf ~/
-```
-
-[Get the code](https://github.com/ggerganov/llama.cpp#get-the-code) & [follow the Linux build instructions](https://github.com/ggerganov/llama.cpp#build) to build `llama.cpp`.
-
-#### Building the Project using Android NDK
-Obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake.
-
-Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux:
-```
-$ mkdir build-android
-$ cd build-android
-$ export NDK=<your_ndk_directory>
-$ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
-$ make
-```
-
-Install [termux](https://github.com/termux/termux-app#installation) on your device and run `termux-setup-storage` to get access to your SD card (if Android 11+ then run the command twice).
-
-Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission:
-
-(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`)
-```
-$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/
-$cd /data/data/com.termux/files/home/bin
-$chmod +x ./*
-```
-
-Download model [llama-2-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_K_M.gguf), and push it to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/`
-
-```
-$mv /sdcard/llama.cpp/llama-2-7b-chat.Q4_K_M.gguf /data/data/com.termux/files/home/model/
-```
-
-Now, you can start chatting:
-```
-$cd /data/data/com.termux/files/home/bin
-$./llama-cli -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml
-```
-
-Here's a demo of an interactive session running on Pixel 5 phone:
-
-https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
-
-### Docker
-
-#### Prerequisites
-* Docker must be installed and running on your system.
-* Create a folder to store big models & intermediate files (ex. /llama/models)
-
-#### Images
-We have three Docker images available for this project:
-
-1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`)
-2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`)
-3. `ghcr.io/ggerganov/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`)
+## Tools
-Additionally, there the following images, similar to the above:
-
-- `ghcr.io/ggerganov/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`)
-- `ghcr.io/ggerganov/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`)
-- `ghcr.io/ggerganov/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`)
-- `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
-- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
-- `ghcr.io/ggerganov/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
-
-The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
-
-#### Usage
-
-The easiest way to download the models, convert them to ggml and optimize them is with the --all-in-one command which includes the full docker image.
-
-Replace `/path/to/models` below with the actual path where you downloaded the models.
-
-```bash
-docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-one "/models/" 7B
-```
-
-On completion, you are ready to play!
-
-```bash
-docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
-```
-
-or with a light image:
-
-```bash
-docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
-```
-
-or with a server image:
-
-```bash
-docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512
-```
-
-### Docker With CUDA
-
-Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container.
-
-#### Building Locally
-
-```bash
-docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile .
-docker build -t local/llama.cpp:light-cuda -f .devops/llama-cli-cuda.Dockerfile .
-docker build -t local/llama.cpp:server-cuda -f .devops/llama-server-cuda.Dockerfile .
-```
-
-You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture.
+### Prepare and Quantize
-The defaults are:
+> [!NOTE]
+> You can use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to quantise your model weights without any setup too. It is synced from `llama.cpp` main every 6 hours.
-- `CUDA_VERSION` set to `11.7.1`
-- `CUDA_DOCKER_ARCH` set to `all`
+To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face.
-The resulting images, are essentially the same as the non-CUDA images:
+Note: `convert.py` has been moved to `examples/convert_legacy_llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derivatives.
+It does not support LLaMA 3, you can use `convert_hf_to_gguf.py` with LLaMA 3 downloaded from Hugging Face.
-1. `local/llama.cpp:full-cuda`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
-2. `local/llama.cpp:light-cuda`: This image only includes the main executable file.
-3. `local/llama.cpp:server-cuda`: This image only includes the server executable file.
+To learn more about quantizing model, [read this documentation](./examples/quantize/README.md)
-#### Usage
+### Perplexity (measuring model quality)
-After building locally, Usage is similar to the non-CUDA examples, but you'll need to add the `--gpus` flag. You will also want to use the `--n-gpu-layers` flag.
+You can use the `perplexity` example to measure perplexity over a given prompt (lower perplexity is better).
+For more information, see [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity).
-```bash
-docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
-docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
-docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1
-```
+To learn more how to measure perplexity using llama.cpp, [read this documentation](./examples/perplexity/README.md)
-### Contributing
+## Contributing
- Contributors can open PRs
- Collaborators can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch
- Make sure to read this: [Inference at the edge](https://github.com/ggerganov/llama.cpp/discussions/205)
- A bit of backstory for those who are interested: [Changelog podcast](https://changelog.com/podcast/532)
-### Docs
+## Other documentations
- [main (cli)](./examples/main/README.md)
- [server](./examples/server/README.md)
- [jeopardy](./examples/jeopardy/README.md)
-- [BLIS](./docs/BLIS.md)
+- [GBNF grammars](./grammars/README.md)
+
+**Development documentations**
+
+- [How to build](./docs/build.md)
+- [Running on Docker](./docs/docker.md)
+- [Build on Android](./docs/android.md)
- [Performance troubleshooting](./docs/token_generation_performance_tips.md)
- [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks)
-- [GBNF grammars](./grammars/README.md)
+++ /dev/null
-BLIS Installation Manual
-------------------------
-
-BLIS is a portable software framework for high-performance BLAS-like dense linear algebra libraries. It has received awards and recognition, including the 2023 James H. Wilkinson Prize for Numerical Software and the 2020 SIAM Activity Group on Supercomputing Best Paper Prize. BLIS provides a new BLAS-like API and a compatibility layer for traditional BLAS routine calls. It offers features such as object-based API, typed API, BLAS and CBLAS compatibility layers.
-
-Project URL: https://github.com/flame/blis
-
-### Prepare:
-
-Compile BLIS:
-
-```bash
-git clone https://github.com/flame/blis
-cd blis
-./configure --enable-cblas -t openmp,pthreads auto
-# will install to /usr/local/ by default.
-make -j
-```
-
-Install BLIS:
-
-```bash
-sudo make install
-```
-
-We recommend using openmp since it's easier to modify the cores being used.
-
-### llama.cpp compilation
-
-Makefile:
-
-```bash
-make GGML_BLIS=1 -j
-# make GGML_BLIS=1 llama-benchmark-matmult
-```
-
-CMake:
-
-```bash
-mkdir build
-cd build
-cmake -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=FLAME ..
-make -j
-```
-
-### llama.cpp execution
-
-According to the BLIS documentation, we could set the following
-environment variables to modify the behavior of openmp:
-
-```bash
-export GOMP_CPU_AFFINITY="0-19"
-export BLIS_NUM_THREADS=14
-```
-
-And then run the binaries as normal.
-
-
-### Intel specific issue
-
-Some might get the error message saying that `libimf.so` cannot be found.
-Please follow this [stackoverflow page](https://stackoverflow.com/questions/70687930/intel-oneapi-2022-libimf-so-no-such-file-or-directory-during-openmpi-compila).
-
-### Reference:
-
-1. https://github.com/flame/blis#getting-started
-2. https://github.com/flame/blis/blob/master/docs/Multithreading.md
+++ /dev/null
-## Add a new model architecture to `llama.cpp`
-
-Adding a model requires few steps:
-
-1. Convert the model to GGUF
-2. Define the model architecture in `llama.cpp`
-3. Build the GGML graph implementation
-
-After following these steps, you can open PR.
-
-Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially:
-- [main](../examples/main)
-- [imatrix](../examples/imatrix)
-- [quantize](../examples/quantize)
-- [server](../examples/server)
-
-### 1. Convert the model to GGUF
-
-This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library.
-Depending on the model architecture, you can use either [convert_hf_to_gguf.py](../convert_hf_to_gguf.py) or [examples/convert_legacy_llama.py](../examples/convert_legacy_llama.py) (for `llama/llama2` models in `.pth` format).
-
-The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors.
-
-The required steps to implement for an HF model are:
-
-1. Define the model `Model.register` annotation in a new `Model` subclass, example:
-
-```python
-@Model.register("MyModelForCausalLM")
-class MyModel(Model):
- model_arch = gguf.MODEL_ARCH.GROK
-```
-
-2. Define the layout of the GGUF tensors in [constants.py](../gguf-py/gguf/constants.py)
-
-Add an enum entry in `MODEL_ARCH`, the model human friendly name in `MODEL_ARCH_NAMES` and the GGUF tensor names in `MODEL_TENSORS`.
-
-Example for `falcon` model:
-```python
- MODEL_ARCH.FALCON: [
- MODEL_TENSOR.TOKEN_EMBD,
- MODEL_TENSOR.OUTPUT_NORM,
- MODEL_TENSOR.OUTPUT,
- MODEL_TENSOR.ATTN_NORM,
- MODEL_TENSOR.ATTN_NORM_2,
- MODEL_TENSOR.ATTN_QKV,
- MODEL_TENSOR.ATTN_OUT,
- MODEL_TENSOR.FFN_DOWN,
- MODEL_TENSOR.FFN_UP,
- ]
-```
-
-3. Map the original tensor names to the standardize equivalent in GGUF
-
-As a general rule, before adding a new tensor name to GGUF, be sure the equivalent naming does not already exist.
-
-Once you have found the GGUF tensor name equivalent, add it to the [tensor_mapping.py](../gguf-py/gguf/tensor_mapping.py) file.
-
-If the tensor name is part of a repetitive layer/block, the key word `bid` substitutes it.
-
-Example for the normalization tensor in attention layers:
-
-```python
-block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
- # Attention norm
- MODEL_TENSOR.ATTN_NORM: (
- "gpt_neox.layers.{bid}.input_layernorm", # gptneox
- "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen
- "transformer.blocks.{bid}.norm_1", # mpt
- ...
- )
-}
-```
-
-`transformer.blocks.{bid}.norm_1` will be mapped to `blk.{bid}.attn_norm` in GGUF.
-
-Depending on the model configuration, tokenizer, code and tensors layout, you will have to override:
-- `Model#set_gguf_parameters`
-- `Model#set_vocab`
-- `Model#write_tensors`
-
-NOTE: Tensor names must end with `.weight` suffix, that is the convention and several tools like `quantize` expect this to proceed the weights.
-
-### 2. Define the model architecture in `llama.cpp`
-
-The model params and tensors layout must be defined in `llama.cpp`:
-1. Define a new `llm_arch`
-2. Define the tensors layout in `LLM_TENSOR_NAMES`
-3. Add any non standard metadata in `llm_load_hparams`
-4. Create the tensors for inference in `llm_load_tensors`
-5. If the model has a RoPE operation, add the rope type in `llama_rope_type`
-
-NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorch` dimensions.
-
-### 3. Build the GGML graph implementation
-
-This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `llama_build_graph`.
-
-Have a look at existing implementation like `build_llama`, `build_dbrx` or `build_bert`.
-
-When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support for missing backend operations can be added in another PR.
-
-Note: to debug the inference graph: you can use [llama-eval-callback](../examples/eval-callback).
-
-## GGUF specification
-
-https://github.com/ggerganov/ggml/blob/master/docs/gguf.md
-
-## Resources
-
-- YaRN RoPE scaling https://github.com/ggerganov/llama.cpp/pull/2268
-- support Baichuan serial models https://github.com/ggerganov/llama.cpp/pull/3009
-- support attention bias https://github.com/ggerganov/llama.cpp/pull/4283
-- Mixtral support https://github.com/ggerganov/llama.cpp/pull/4406
-- BERT embeddings https://github.com/ggerganov/llama.cpp/pull/5423
-- Grok-1 support https://github.com/ggerganov/llama.cpp/pull/6204
-- Command R Plus support https://github.com/ggerganov/llama.cpp/pull/6491
-- support arch DBRX https://github.com/ggerganov/llama.cpp/pull/6515
-- How to convert HuggingFace model to GGUF format https://github.com/ggerganov/llama.cpp/discussions/2948
--- /dev/null
+
+# Android
+
+## Build on Android using Termux
+[Termux](https://github.com/termux/termux-app#installation) is a method to execute `llama.cpp` on an Android device (no root required).
+```
+apt update && apt upgrade -y
+apt install git make cmake
+```
+
+It's recommended to move your model inside the `~/` directory for best performance:
+```
+cd storage/downloads
+mv model.gguf ~/
+```
+
+[Get the code](https://github.com/ggerganov/llama.cpp#get-the-code) & [follow the Linux build instructions](https://github.com/ggerganov/llama.cpp#build) to build `llama.cpp`.
+
+## Building the Project using Android NDK
+Obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake.
+
+Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux:
+```
+$ mkdir build-android
+$ cd build-android
+$ export NDK=<your_ndk_directory>
+$ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
+$ make
+```
+
+Install [termux](https://github.com/termux/termux-app#installation) on your device and run `termux-setup-storage` to get access to your SD card (if Android 11+ then run the command twice).
+
+Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission:
+
+(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`)
+```
+$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/
+$cd /data/data/com.termux/files/home/bin
+$chmod +x ./*
+```
+
+Download model [llama-2-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_K_M.gguf), and push it to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/`
+
+```
+$mv /sdcard/llama.cpp/llama-2-7b-chat.Q4_K_M.gguf /data/data/com.termux/files/home/model/
+```
+
+Now, you can start chatting:
+```
+$cd /data/data/com.termux/files/home/bin
+$./llama-cli -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml
+```
+
+Here's a demo of an interactive session running on Pixel 5 phone:
+
+https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
--- /dev/null
+BLIS Installation Manual
+------------------------
+
+BLIS is a portable software framework for high-performance BLAS-like dense linear algebra libraries. It has received awards and recognition, including the 2023 James H. Wilkinson Prize for Numerical Software and the 2020 SIAM Activity Group on Supercomputing Best Paper Prize. BLIS provides a new BLAS-like API and a compatibility layer for traditional BLAS routine calls. It offers features such as object-based API, typed API, BLAS and CBLAS compatibility layers.
+
+Project URL: https://github.com/flame/blis
+
+### Prepare:
+
+Compile BLIS:
+
+```bash
+git clone https://github.com/flame/blis
+cd blis
+./configure --enable-cblas -t openmp,pthreads auto
+# will install to /usr/local/ by default.
+make -j
+```
+
+Install BLIS:
+
+```bash
+sudo make install
+```
+
+We recommend using openmp since it's easier to modify the cores being used.
+
+### llama.cpp compilation
+
+Makefile:
+
+```bash
+make GGML_BLIS=1 -j
+# make GGML_BLIS=1 llama-benchmark-matmult
+```
+
+CMake:
+
+```bash
+mkdir build
+cd build
+cmake -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=FLAME ..
+make -j
+```
+
+### llama.cpp execution
+
+According to the BLIS documentation, we could set the following
+environment variables to modify the behavior of openmp:
+
+```bash
+export GOMP_CPU_AFFINITY="0-19"
+export BLIS_NUM_THREADS=14
+```
+
+And then run the binaries as normal.
+
+
+### Intel specific issue
+
+Some might get the error message saying that `libimf.so` cannot be found.
+Please follow this [stackoverflow page](https://stackoverflow.com/questions/70687930/intel-oneapi-2022-libimf-so-no-such-file-or-directory-during-openmpi-compila).
+
+### Reference:
+
+1. https://github.com/flame/blis#getting-started
+2. https://github.com/flame/blis/blob/master/docs/Multithreading.md
--- /dev/null
+# llama.cpp for SYCL
+
+- [Background](#background)
+- [Recommended Release](#recommended-release)
+- [News](#news)
+- [OS](#os)
+- [Hardware](#hardware)
+- [Docker](#docker)
+- [Linux](#linux)
+- [Windows](#windows)
+- [Environment Variable](#environment-variable)
+- [Known Issue](#known-issues)
+- [Q&A](#qa)
+- [TODO](#todo)
+
+## Background
+
+**SYCL** is a high-level parallel programming model designed to improve developers productivity writing code across various hardware accelerators such as CPUs, GPUs, and FPGAs. It is a single-source language designed for heterogeneous computing and based on standard C++17.
+
+**oneAPI** is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include:
+
+- **DPCPP** *(Data Parallel C++)*: The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers.
+- **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. oneMKL - Math Kernel Library)*.
+- **oneAPI LevelZero**: A high performance low level interface for fine-grained control over intel iGPUs and dGPUs.
+- **Nvidia & AMD Plugins**: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets.
+
+### Llama.cpp + SYCL
+
+The llama.cpp SYCL backend is designed to support **Intel GPU** firstly. Based on the cross-platform feature of SYCL, it could support other vendor GPUs: Nvidia GPU (*AMD GPU coming*).
+
+When targeting **Intel CPU**, it is recommended to use llama.cpp for [Intel oneMKL](README.md#intel-onemkl) backend.
+
+It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose.
+
+## Recommended Release
+
+The SYCL backend would be broken by some PRs due to no online CI.
+
+The following release is verified with good quality:
+
+|Commit ID|Tag|Release|Verified Platform|
+|-|-|-|-|
+|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggerganov/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1|
+
+
+## News
+
+- 2024.5
+ - Performance is increased: 34 -> 37 tokens/s of llama-2-7b.Q4_0 on Arc770.
+ - Arch Linux is verified successfully.
+
+- 2024.4
+ - Support data types: GGML_TYPE_IQ4_NL, GGML_TYPE_IQ4_XS, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ3_S, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M.
+
+- 2024.3
+ - Release binary files of Windows.
+ - A blog is published: **Run LLM on all Intel GPUs Using llama.cpp**: [intel.com](https://www.intel.com/content/www/us/en/developer/articles/technical/run-llm-on-all-gpus-using-llama-cpp-artical.html) or [medium.com](https://medium.com/@jianyu_neo/run-llm-on-all-intel-gpus-using-llama-cpp-fd2e2dcbd9bd).
+ - New base line is ready: [tag b2437](https://github.com/ggerganov/llama.cpp/tree/b2437).
+ - Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing.
+ - Support to assign main GPU by **--main-gpu**, replace $GGML_SYCL_DEVICE.
+ - Support detecting all GPUs with level-zero and same top **Max compute units**.
+ - Support OPs
+ - hardsigmoid
+ - hardswish
+ - pool2d
+
+- 2024.1
+ - Create SYCL backend for Intel GPU.
+ - Support Windows build
+
+## OS
+
+| OS | Status | Verified |
+|---------|---------|------------------------------------------------|
+| Linux | Support | Ubuntu 22.04, Fedora Silverblue 39, Arch Linux |
+| Windows | Support | Windows 11 |
+
+
+## Hardware
+
+### Intel GPU
+
+**Verified devices**
+
+| Intel GPU | Status | Verified Model |
+|-------------------------------|---------|---------------------------------------|
+| Intel Data Center Max Series | Support | Max 1550, 1100 |
+| Intel Data Center Flex Series | Support | Flex 170 |
+| Intel Arc Series | Support | Arc 770, 730M, Arc A750 |
+| Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake |
+| Intel iGPU | Support | iGPU in i5-1250P, i7-1260P, i7-1165G7 |
+
+*Notes:*
+
+- **Memory**
+ - The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/llama-cli`.
+
+ - Please make sure the GPU shared memory from the host is large enough to account for the model's size. For e.g. the *llama-2-7b.Q4_0* requires at least 8.0GB for integrated GPU and 4.0GB for discrete GPU.
+
+- **Execution Unit (EU)**
+ - If the iGPU has less than 80 EUs, the inference speed will likely be too slow for practical use.
+
+### Other Vendor GPU
+
+**Verified devices**
+
+| Nvidia GPU | Status | Verified Model |
+|--------------------------|---------|----------------|
+| Ampere Series | Support | A100, A4000 |
+| Ampere Series *(Mobile)* | Support | RTX 40 Series |
+
+## Docker
+The docker build option is currently limited to *intel GPU* targets.
+
+### Build image
+```sh
+# Using FP16
+docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" -f .devops/llama-cli-intel.Dockerfile .
+```
+
+*Notes*:
+
+To build in default FP32 *(Slower than FP16 alternative)*, you can remove the `--build-arg="GGML_SYCL_F16=ON"` argument from the previous command.
+
+You can also use the `.devops/llama-server-intel.Dockerfile`, which builds the *"server"* alternative.
+
+### Run container
+
+```sh
+# First, find all the DRI cards
+ls -la /dev/dri
+# Then, pick the card that you want to use (here for e.g. /dev/dri/card1).
+docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
+```
+
+*Notes:*
+- Docker has been tested successfully on native Linux. WSL support has not been verified yet.
+- You may need to install Intel GPU driver on the **host** machine *(Please refer to the [Linux configuration](#linux) for details)*.
+
+## Linux
+
+### I. Setup Environment
+
+1. **Install GPU drivers**
+
+ - **Intel GPU**
+
+Intel data center GPUs drivers installation guide and download page can be found here: [Get intel dGPU Drivers](https://dgpu-docs.intel.com/driver/installation.html#ubuntu-install-steps).
+
+*Note*: for client GPUs *(iGPU & Arc A-Series)*, please refer to the [client iGPU driver installation](https://dgpu-docs.intel.com/driver/client/overview.html).
+
+Once installed, add the user(s) to the `video` and `render` groups.
+
+```sh
+sudo usermod -aG render $USER
+sudo usermod -aG video $USER
+```
+
+*Note*: logout/re-login for the changes to take effect.
+
+Verify installation through `clinfo`:
+
+```sh
+sudo apt install clinfo
+sudo clinfo -l
+```
+
+Sample output:
+
+```sh
+Platform #0: Intel(R) OpenCL Graphics
+ `-- Device #0: Intel(R) Arc(TM) A770 Graphics
+
+Platform #0: Intel(R) OpenCL HD Graphics
+ `-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49]
+```
+
+- **Nvidia GPU**
+
+In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements *-found [here](README.md#cuda)-* are installed.
+
+2. **Install Intel® oneAPI Base toolkit**
+
+- **For Intel GPU**
+
+The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.
+
+Please follow the instructions for downloading and installing the Toolkit for Linux, and preferably keep the default installation values unchanged, notably the installation path *(`/opt/intel/oneapi` by default)*.
+
+Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.
+
+Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI MKL for intel GPUs.
+
+- **Adding support to Nvidia GPUs**
+
+**oneAPI Plugin**: In order to enable SYCL support on Nvidia GPUs, please install the [Codeplay oneAPI Plugin for Nvidia GPUs](https://developer.codeplay.com/products/oneapi/nvidia/download). User should also make sure the plugin version matches the installed base toolkit one *(previous step)* for a seamless "oneAPI on Nvidia GPU" setup.
+
+
+**oneMKL for cuBlas**: The current oneMKL releases *(shipped with the oneAPI base-toolkit)* do not contain the cuBLAS backend. A build from source of the upstream [oneMKL](https://github.com/oneapi-src/oneMKL) with the *cuBLAS* backend enabled is thus required to run it on Nvidia GPUs.
+
+```sh
+git clone https://github.com/oneapi-src/oneMKL
+cd oneMKL
+cmake -B buildWithCublas -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON -DTARGET_DOMAINS=blas
+cmake --build buildWithCublas --config Release
+```
+
+
+3. **Verify installation and environment**
+
+In order to check the available SYCL devices on the machine, please use the `sycl-ls` command.
+```sh
+source /opt/intel/oneapi/setvars.sh
+sycl-ls
+```
+
+- **Intel GPU**
+
+When targeting an intel GPU, the user should expect one or more level-zero devices among the available SYCL devices. Please make sure that at least one GPU is present, for instance [`ext_oneapi_level_zero:gpu:0`] in the sample output below:
+
+```
+[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000]
+[opencl:cpu:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i7-13700K OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
+[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO [23.30.26918.50]
+[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26918]
+```
+
+- **Nvidia GPU**
+
+Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA device [`ext_oneapi_cuda:gpu`] as bellow:
+```
+[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.12.0.12_195853.xmain-hotfix]
+[opencl:cpu:1] Intel(R) OpenCL, Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz OpenCL 3.0 (Build 0) [2023.16.12.0.12_195853.xmain-hotfix]
+[ext_oneapi_cuda:gpu:0] NVIDIA CUDA BACKEND, NVIDIA A100-PCIE-40GB 8.0 [CUDA 12.2]
+```
+
+### II. Build llama.cpp
+
+#### Intel GPU
+```sh
+# Export relevant ENV variables
+source /opt/intel/oneapi/setvars.sh
+
+# Build LLAMA with MKL BLAS acceleration for intel GPU
+
+# Option 1: Use FP32 (recommended for better performance in most cases)
+cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
+
+# Option 2: Use FP16
+cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON
+
+# build all binary
+cmake --build build --config Release -j -v
+```
+
+#### Nvidia GPU
+```sh
+# Export relevant ENV variables
+export LD_LIBRARY_PATH=/path/to/oneMKL/buildWithCublas/lib:$LD_LIBRARY_PATH
+export LIBRARY_PATH=/path/to/oneMKL/buildWithCublas/lib:$LIBRARY_PATH
+export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithCublas/include:$CPLUS_INCLUDE_DIR
+export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR
+
+# Build LLAMA with Nvidia BLAS acceleration through SYCL
+
+# Option 1: Use FP32 (recommended for better performance in most cases)
+cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
+
+# Option 2: Use FP16
+cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON
+
+# build all binary
+cmake --build build --config Release -j -v
+
+```
+
+### III. Run the inference
+
+1. Retrieve and prepare model
+
+You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
+
+2. Enable oneAPI running environment
+
+```sh
+source /opt/intel/oneapi/setvars.sh
+```
+
+3. List devices information
+
+Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:
+
+```sh
+./build/bin/llama-ls-sycl-device
+```
+A example of such log in a system with 1 *intel CPU* and 1 *intel GPU* can look like the following:
+```
+found 6 SYCL devices:
+| | | |Compute |Max compute|Max work|Max sub| |
+|ID| Device Type| Name|capability|units |group |group |Global mem size|
+|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
+| 0|[level_zero:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 1.3| 512| 1024| 32| 16225243136|
+| 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216|
+| 2| [opencl:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 3.0| 512| 1024| 32| 16225243136|
+| 3| [opencl:gpu:1]| Intel(R) UHD Graphics 770| 3.0| 32| 512| 32| 53651849216|
+| 4| [opencl:cpu:0]| 13th Gen Intel(R) Core(TM) i7-13700K| 3.0| 24| 8192| 64| 67064815616|
+| 5| [opencl:acc:0]| Intel(R) FPGA Emulation Device| 1.2| 24|67108864| 64| 67064815616|
+```
+
+| Attribute | Note |
+|------------------------|-------------------------------------------------------------|
+| compute capability 1.3 | Level-zero driver/runtime, recommended |
+| compute capability 3.0 | OpenCL driver/runtime, slower than level-zero in most cases |
+
+4. Launch inference
+
+There are two device selection modes:
+
+- Single device: Use one device target specified by the user.
+- Multiple devices: Automatically select the devices with the same largest Max compute-units.
+
+| Device selection | Parameter |
+|------------------|----------------------------------------|
+| Single device | --split-mode none --main-gpu DEVICE_ID |
+| Multiple devices | --split-mode layer (default) |
+
+Examples:
+
+- Use device 0:
+
+```sh
+ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0
+```
+or run by script:
+
+```sh
+./examples/sycl/run_llama2.sh 0
+```
+
+- Use multiple devices:
+
+```sh
+ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer
+```
+
+Otherwise, you can run the script:
+
+```sh
+./examples/sycl/run_llama2.sh
+```
+
+*Notes:*
+
+- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:
+
+```sh
+detect 1 SYCL GPUs: [0] with top Max compute units:512
+```
+Or
+```sh
+use 1 SYCL GPUs: [0] with Max compute units:512
+```
+
+## Windows
+
+### I. Setup Environment
+
+1. Install GPU driver
+
+Intel GPU drivers instructions guide and download page can be found here: [Get intel GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html).
+
+2. Install Visual Studio
+
+If you already have a recent version of Microsoft Visual Studio, you can skip this step. Otherwise, please refer to the official download page for [Microsoft Visual Studio](https://visualstudio.microsoft.com/).
+
+3. Install Intel® oneAPI Base toolkit
+
+The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.
+
+Please follow the instructions for downloading and installing the Toolkit for Windows, and preferably keep the default installation values unchanged, notably the installation path *(`C:\Program Files (x86)\Intel\oneAPI` by default)*.
+
+Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.
+
+b. Enable oneAPI running environment:
+
+- Type "oneAPI" in the search bar, then open the `Intel oneAPI command prompt for Intel 64 for Visual Studio 2022` App.
+
+- On the command prompt, enable the runtime environment with the following:
+```
+"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
+```
+
+c. Verify installation
+
+In the oneAPI command line, run the following to print the available SYCL devices:
+
+```
+sycl-ls
+```
+
+There should be one or more *level-zero* GPU devices displayed as **[ext_oneapi_level_zero:gpu]**. Below is example of such output detecting an *intel Iris Xe* GPU as a Level-zero SYCL device:
+
+Output (example):
+```
+[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000]
+[opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
+[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO [31.0.101.5186]
+[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044]
+```
+
+4. Install build tools
+
+a. Download & install cmake for Windows: https://cmake.org/download/ (CMake can also be installed from Visual Studio Installer)
+b. The new Visual Studio will install Ninja as default. (If not, please install it manually: https://ninja-build.org/)
+
+
+### II. Build llama.cpp
+
+On the oneAPI command line window, step into the llama.cpp main directory and run the following:
+
+```
+@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
+
+# Option 1: Use FP32 (recommended for better performance in most cases)
+cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release
+
+# Option 2: Or FP16
+cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DGGML_SYCL_F16=ON
+
+cmake --build build --config Release -j
+```
+
+Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions:
+```sh
+.\examples\sycl\win-build-sycl.bat
+```
+
+Or, use CMake presets to build:
+```sh
+cmake --preset x64-windows-sycl-release
+cmake --build build-x64-windows-sycl-release -j --target llama-cli
+
+cmake -DGGML_SYCL_F16=ON --preset x64-windows-sycl-release
+cmake --build build-x64-windows-sycl-release -j --target llama-cli
+
+cmake --preset x64-windows-sycl-debug
+cmake --build build-x64-windows-sycl-debug -j --target llama-cli
+```
+
+Or, you can use Visual Studio to open llama.cpp folder as a CMake project. Choose the sycl CMake presets (`x64-windows-sycl-release` or `x64-windows-sycl-debug`) before you compile the project.
+
+*Notes:*
+
+- In case of a minimal experimental setup, the user can build the inference executable only through `cmake --build build --config Release -j --target llama-cli`.
+
+### III. Run the inference
+
+1. Retrieve and prepare model
+
+You can refer to the general [*Prepare and Quantize*](README#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
+
+2. Enable oneAPI running environment
+
+On the oneAPI command line window, run the following and step into the llama.cpp directory:
+```
+"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
+```
+
+3. List devices information
+
+Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:
+
+```
+build\bin\ls-sycl-device.exe
+```
+
+The output of this command in a system with 1 *intel CPU* and 1 *intel GPU* would look like the following:
+```
+found 6 SYCL devices:
+| | | |Compute |Max compute|Max work|Max sub| |
+|ID| Device Type| Name|capability|units |group |group |Global mem size|
+|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
+| 0|[level_zero:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 1.3| 512| 1024| 32| 16225243136|
+| 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216|
+| 2| [opencl:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 3.0| 512| 1024| 32| 16225243136|
+| 3| [opencl:gpu:1]| Intel(R) UHD Graphics 770| 3.0| 32| 512| 32| 53651849216|
+| 4| [opencl:cpu:0]| 13th Gen Intel(R) Core(TM) i7-13700K| 3.0| 24| 8192| 64| 67064815616|
+| 5| [opencl:acc:0]| Intel(R) FPGA Emulation Device| 1.2| 24|67108864| 64| 67064815616|
+
+```
+
+| Attribute | Note |
+|------------------------|-----------------------------------------------------------|
+| compute capability 1.3 | Level-zero running time, recommended |
+| compute capability 3.0 | OpenCL running time, slower than level-zero in most cases |
+
+
+4. Launch inference
+
+There are two device selection modes:
+
+- Single device: Use one device assigned by user.
+- Multiple devices: Automatically choose the devices with the same biggest Max compute units.
+
+| Device selection | Parameter |
+|------------------|----------------------------------------|
+| Single device | --split-mode none --main-gpu DEVICE_ID |
+| Multiple devices | --split-mode layer (default) |
+
+Examples:
+
+- Use device 0:
+
+```
+build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm none -mg 0
+```
+
+- Use multiple devices:
+
+```
+build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm layer
+```
+Otherwise, run the following wrapper script:
+
+```
+.\examples\sycl\win-run-llama2.bat
+```
+
+Note:
+
+- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:
+
+```sh
+detect 1 SYCL GPUs: [0] with top Max compute units:512
+```
+Or
+```sh
+use 1 SYCL GPUs: [0] with Max compute units:512
+```
+
+## Environment Variable
+
+#### Build
+
+| Name | Value | Function |
+|--------------------|-----------------------------------|---------------------------------------------|
+| GGML_SYCL | ON (mandatory) | Enable build with SYCL code path. |
+| GGML_SYCL_TARGET | INTEL *(default)* \| NVIDIA | Set the SYCL target device type. |
+| GGML_SYCL_F16 | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path. |
+| CMAKE_C_COMPILER | icx | Set *icx* compiler for SYCL code path. |
+| CMAKE_CXX_COMPILER | icpx *(Linux)*, icx *(Windows)* | Set `icpx/icx` compiler for SYCL code path. |
+
+#### Runtime
+
+| Name | Value | Function |
+|-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------|
+| GGML_SYCL_DEBUG | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG |
+| ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
+
+## Known Issues
+
+- `Split-mode:[row]` is not supported.
+
+## Q&A
+
+- Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`.
+
+ - Potential cause: Unavailable oneAPI installation or not set ENV variables.
+ - Solution: Install *oneAPI base toolkit* and enable its ENV through: `source /opt/intel/oneapi/setvars.sh`.
+
+- General compiler error:
+
+ - Remove **build** folder or try a clean-build.
+
+- I can **not** see `[ext_oneapi_level_zero:gpu]` afer installing the GPU driver on Linux.
+
+ Please double-check with `sudo sycl-ls`.
+
+ If it's present in the list, please add video/render group to your user then **logout/login** or restart your system:
+
+ ```
+ sudo usermod -aG render $USER
+ sudo usermod -aG video $USER
+ ```
+ Otherwise, please double-check the GPU driver installation steps.
+
+### **GitHub contribution**:
+Please add the **[SYCL]** prefix/tag in issues/PRs titles to help the SYCL-team check/address them without delay.
+
+## TODO
+
+- Support row layer split for multiple card runs.
--- /dev/null
+# Build llama.cpp locally
+
+**To get the Code:**
+
+```bash
+git clone https://github.com/ggerganov/llama.cpp
+cd llama.cpp
+```
+
+In order to build llama.cpp you have four different options.
+
+- Using `make`:
+ - On Linux or MacOS:
+
+ ```bash
+ make
+ ```
+
+ - On Windows:
+
+ 1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
+ 2. Extract `w64devkit` on your pc.
+ 3. Run `w64devkit.exe`.
+ 4. Use the `cd` command to reach the `llama.cpp` folder.
+ 5. From here you can run:
+ ```bash
+ make
+ ```
+
+ - Notes:
+ - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `make -j 8` will run 8 jobs in parallel.
+ - For faster repeated compilation, install [ccache](https://ccache.dev/).
+ - For debug builds, run `make LLAMA_DEBUG=1`
+
+- Using `CMake`:
+
+ ```bash
+ cmake -B build
+ cmake --build build --config Release
+ ```
+
+ **Notes**:
+
+ - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel.
+ - For faster repeated compilation, install [ccache](https://ccache.dev/).
+ - For debug builds, there are two cases:
+
+ 1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag):
+
+ ```bash
+ cmake -B build -DCMAKE_BUILD_TYPE=Debug
+ cmake --build build
+ ```
+
+ 2. Multi-config generators (`-G` param set to Visual Studio, XCode...):
+
+ ```bash
+ cmake -B build -G "Xcode"
+ cmake --build build --config Debug
+ ```
+
+- Using `gmake` (FreeBSD):
+
+ 1. Install and activate [DRM in FreeBSD](https://wiki.freebsd.org/Graphics)
+ 2. Add your user to **video** group
+ 3. Install compilation dependencies.
+
+ ```bash
+ sudo pkg install gmake automake autoconf pkgconf llvm15 openblas
+
+ gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4
+ ```
+
+## Metal Build
+
+On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
+To disable the Metal build at compile time use the `GGML_NO_METAL=1` flag or the `GGML_METAL=OFF` cmake option.
+
+When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers|-ngl 0` command-line
+argument.
+
+## BLAS Build
+
+Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS. There are currently several different BLAS implementations available for build and use:
+
+### Accelerate Framework:
+
+ This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.
+
+### OpenBLAS:
+
+This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.
+
+- Using `make`:
+ - On Linux:
+ ```bash
+ make GGML_OPENBLAS=1
+ ```
+
+ - On Windows:
+
+ 1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
+ 2. Download the latest version of [OpenBLAS for Windows](https://github.com/xianyi/OpenBLAS/releases).
+ 3. Extract `w64devkit` on your pc.
+ 4. From the OpenBLAS zip that you just downloaded copy `libopenblas.a`, located inside the `lib` folder, inside `w64devkit\x86_64-w64-mingw32\lib`.
+ 5. From the same OpenBLAS zip copy the content of the `include` folder inside `w64devkit\x86_64-w64-mingw32\include`.
+ 6. Run `w64devkit.exe`.
+ 7. Use the `cd` command to reach the `llama.cpp` folder.
+ 8. From here you can run:
+
+ ```bash
+ make GGML_OPENBLAS=1
+ ```
+
+- Using `CMake` on Linux:
+
+ ```bash
+ cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
+ cmake --build build --config Release
+ ```
+
+### BLIS
+
+Check [BLIS.md](./backend/BLIS.md) for more information.
+
+### SYCL
+
+SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators.
+
+llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).
+
+For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md).
+
+### Intel oneMKL
+
+Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./backend/SYCL.md).
+
+- Using manual oneAPI installation:
+ By default, `GGML_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DGGML_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:
+ ```bash
+ source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation
+ cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_NATIVE=ON
+ cmake --build build --config Release
+ ```
+
+- Using oneAPI docker image:
+ If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above.
+
+Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information.
+
+### CUDA
+
+This provides GPU acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
+
+For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling.
+
+- Using `make`:
+ ```bash
+ make GGML_CUDA=1
+ ```
+- Using `CMake`:
+
+ ```bash
+ cmake -B build -DGGML_CUDA=ON
+ cmake --build build --config Release
+ ```
+
+The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance:
+
+| Option | Legal values | Default | Description |
+|-------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| GGML_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
+| GGML_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
+| GGML_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. |
+| GGML_CUDA_FORCE_MMQ | Boolean | false | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, RDNA3). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower. |
+| GGML_CUDA_FORCE_CUBLAS | Boolean | false | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models |
+| GGML_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. |
+| GGML_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
+| GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. |
+| GGML_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. |
+
+### hipBLAS
+
+This provides BLAS acceleration on HIP-supported AMD GPUs.
+Make sure to have ROCm installed.
+You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html#rocm-install-quick).
+
+- Using `make`:
+ ```bash
+ make GGML_HIPBLAS=1
+ ```
+- Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
+ ```bash
+ HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
+ cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
+ && cmake --build build --config Release -- -j 16
+ ```
+ On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DGGML_HIP_UMA=ON`.
+ However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
+
+ Note that if you get the following error:
+ ```
+ clang: error: cannot find ROCm device library; provide its path via '--rocm-path' or '--rocm-device-lib-path', or pass '-nogpulib' to build without ROCm device library
+ ```
+ Try searching for a directory under `HIP_PATH` that contains the file
+ `oclc_abi_version_400.bc`. Then, add the following to the start of the
+ command: `HIP_DEVICE_LIB_PATH=<directory-you-just-found>`, so something
+ like:
+ ```bash
+ HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \
+ HIP_DEVICE_LIB_PATH=<directory-you-just-found> \
+ cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
+ && cmake --build build -- -j 16
+ ```
+
+- Using `make` (example for target gfx1030, build with 16 CPU threads):
+ ```bash
+ make -j16 GGML_HIPBLAS=1 GGML_HIP_UMA=1 AMDGPU_TARGETS=gfx1030
+ ```
+
+- Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU):
+ ```bash
+ set PATH=%HIP_PATH%\bin;%PATH%
+ cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release
+ cmake --build build
+ ```
+ Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors)
+ Find your gpu version string by matching the most significant version information from `rocminfo | grep gfx | head -1 | awk '{print $2}'` with the list of processors, e.g. `gfx1035` maps to `gfx1030`.
+
+
+The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
+If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3.
+The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above):
+
+| Option | Legal values | Default | Description |
+|------------------------|------------------------|---------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| GGML_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
+| GGML_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
+| GGML_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
+
+### Vulkan
+
+**With docker**:
+
+You don't need to install Vulkan SDK. It will be installed inside the container.
+
+```sh
+# Build the image
+docker build -t llama-cpp-vulkan -f .devops/llama-cli-vulkan.Dockerfile .
+
+# Then, use it:
+docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
+```
+
+**Without docker**:
+
+Firstly, you need to make sure you have installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html)
+
+For example, on Ubuntu 22.04 (jammy), use the command below:
+
+```bash
+wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add -
+wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
+apt update -y
+apt-get install -y vulkan-sdk
+# To verify the installation, use the command below:
+vulkaninfo
+```
+
+Alternatively your package manager might be able to provide the appropriate libraries.
+For example for Ubuntu 22.04 you can install `libvulkan-dev` instead.
+For Fedora 40, you can install `vulkan-devel`, `glslc` and `glslang` packages.
+
+Then, build llama.cpp using the cmake command below:
+
+```bash
+cmake -B build -DGGML_VULKAN=1
+cmake --build build --config Release
+# Test the output binary (with "-ngl 33" to offload all layers to GPU)
+./bin/llama-cli -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4
+
+# You should see in the output, ggml_vulkan detected your GPU. For example:
+# ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32
+```
+
+### Android
+
+To read documentation for how to build on Android, [click here](./android.md)
+++ /dev/null
-# Debugging Tests Tips
-
-## How to run & execute or debug a specific test without anything else to keep the feedback loop short?
-
-There is a script called debug-test.sh in the scripts folder whose parameter takes a REGEX and an optional test number.
-
-For example, running the following command will output an interactive list from which you can select a test. It takes this form:
-
-`debug-test.sh [OPTION]... <test_regex> <test_number>`
-
-It will then build & run in the debugger for you.
-
-To just execute a test and get back a PASS or FAIL message run:
-
-```bash
-./scripts/debug-test.sh test-tokenizer
-```
-
-To test in GDB use the `-g` flag to enable gdb test mode.
-
-```bash
-./scripts/debug-test.sh -g test-tokenizer
-
-# Once in the debugger, i.e. at the chevrons prompt, setting a breakpoint could be as follows:
->>> b main
-```
-
-To speed up the testing loop, if you know your test number you can just run it similar to below:
-
-```bash
-./scripts/debug-test.sh test 23
-```
-
-For further reference use `debug-test.sh -h` to print help.
-
-
-
-### How does the script work?
-If you want to be able to use the concepts contained in the script separately, the important ones are briefly outlined below.
-
-#### Step 1: Reset and Setup folder context
-
-From base of this repository, let's create `build-ci-debug` as our build context.
-
-```bash
-rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug
-```
-
-#### Step 2: Setup Build Environment and Compile Test Binaries
-
-Setup and trigger a build under debug mode. You may adapt the arguments as needed, but in this case these are sane defaults.
-
-```bash
-cmake -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON ..
-make -j
-```
-
-#### Step 3: Find all tests available that matches REGEX
-
-The output of this command will give you the command & arguments needed to run GDB.
-
-* `-R test-tokenizer` : looks for all the test files named `test-tokenizer*` (R=Regex)
-* `-N` : "show-only" disables test execution & shows test commands that you can feed to GDB.
-* `-V` : Verbose Mode
-
-```bash
-ctest -R "test-tokenizer" -V -N
-```
-
-This may return output similar to below (focusing on key lines to pay attention to):
-
-```bash
-...
-1: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf"
-1: Working Directory: .
-Labels: main
- Test #1: test-tokenizer-0-llama-spm
-...
-4: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-falcon.gguf"
-4: Working Directory: .
-Labels: main
- Test #4: test-tokenizer-0-falcon
-...
-```
-
-#### Step 4: Identify Test Command for Debugging
-
-So for test #1 above we can tell these two pieces of relevant information:
-* Test Binary: `~/llama.cpp/build-ci-debug/bin/test-tokenizer-0`
-* Test GGUF Model: `~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf`
-
-#### Step 5: Run GDB on test command
-
-Based on the ctest 'test command' report above we can then run a gdb session via this command below:
-
-```bash
-gdb --args ${Test Binary} ${Test GGUF Model}
-```
-
-Example:
-
-```bash
-gdb --args ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf"
-```
--- /dev/null
+# Add a new model architecture to `llama.cpp`
+
+Adding a model requires few steps:
+
+1. Convert the model to GGUF
+2. Define the model architecture in `llama.cpp`
+3. Build the GGML graph implementation
+
+After following these steps, you can open PR.
+
+Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially:
+- [main](../examples/main)
+- [imatrix](../examples/imatrix)
+- [quantize](../examples/quantize)
+- [server](../examples/server)
+
+### 1. Convert the model to GGUF
+
+This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library.
+Depending on the model architecture, you can use either [convert_hf_to_gguf.py](../convert_hf_to_gguf.py) or [examples/convert_legacy_llama.py](../examples/convert_legacy_llama.py) (for `llama/llama2` models in `.pth` format).
+
+The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors.
+
+The required steps to implement for an HF model are:
+
+1. Define the model `Model.register` annotation in a new `Model` subclass, example:
+
+```python
+@Model.register("MyModelForCausalLM")
+class MyModel(Model):
+ model_arch = gguf.MODEL_ARCH.GROK
+```
+
+2. Define the layout of the GGUF tensors in [constants.py](../gguf-py/gguf/constants.py)
+
+Add an enum entry in `MODEL_ARCH`, the model human friendly name in `MODEL_ARCH_NAMES` and the GGUF tensor names in `MODEL_TENSORS`.
+
+Example for `falcon` model:
+```python
+ MODEL_ARCH.FALCON: [
+ MODEL_TENSOR.TOKEN_EMBD,
+ MODEL_TENSOR.OUTPUT_NORM,
+ MODEL_TENSOR.OUTPUT,
+ MODEL_TENSOR.ATTN_NORM,
+ MODEL_TENSOR.ATTN_NORM_2,
+ MODEL_TENSOR.ATTN_QKV,
+ MODEL_TENSOR.ATTN_OUT,
+ MODEL_TENSOR.FFN_DOWN,
+ MODEL_TENSOR.FFN_UP,
+ ]
+```
+
+3. Map the original tensor names to the standardize equivalent in GGUF
+
+As a general rule, before adding a new tensor name to GGUF, be sure the equivalent naming does not already exist.
+
+Once you have found the GGUF tensor name equivalent, add it to the [tensor_mapping.py](../gguf-py/gguf/tensor_mapping.py) file.
+
+If the tensor name is part of a repetitive layer/block, the key word `bid` substitutes it.
+
+Example for the normalization tensor in attention layers:
+
+```python
+block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
+ # Attention norm
+ MODEL_TENSOR.ATTN_NORM: (
+ "gpt_neox.layers.{bid}.input_layernorm", # gptneox
+ "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen
+ "transformer.blocks.{bid}.norm_1", # mpt
+ ...
+ )
+}
+```
+
+`transformer.blocks.{bid}.norm_1` will be mapped to `blk.{bid}.attn_norm` in GGUF.
+
+Depending on the model configuration, tokenizer, code and tensors layout, you will have to override:
+- `Model#set_gguf_parameters`
+- `Model#set_vocab`
+- `Model#write_tensors`
+
+NOTE: Tensor names must end with `.weight` suffix, that is the convention and several tools like `quantize` expect this to proceed the weights.
+
+### 2. Define the model architecture in `llama.cpp`
+
+The model params and tensors layout must be defined in `llama.cpp`:
+1. Define a new `llm_arch`
+2. Define the tensors layout in `LLM_TENSOR_NAMES`
+3. Add any non standard metadata in `llm_load_hparams`
+4. Create the tensors for inference in `llm_load_tensors`
+5. If the model has a RoPE operation, add the rope type in `llama_rope_type`
+
+NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorch` dimensions.
+
+### 3. Build the GGML graph implementation
+
+This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `llama_build_graph`.
+
+Have a look at existing implementation like `build_llama`, `build_dbrx` or `build_bert`.
+
+When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support for missing backend operations can be added in another PR.
+
+Note: to debug the inference graph: you can use [llama-eval-callback](../examples/eval-callback).
+
+## GGUF specification
+
+https://github.com/ggerganov/ggml/blob/master/docs/gguf.md
+
+## Resources
+
+- YaRN RoPE scaling https://github.com/ggerganov/llama.cpp/pull/2268
+- support Baichuan serial models https://github.com/ggerganov/llama.cpp/pull/3009
+- support attention bias https://github.com/ggerganov/llama.cpp/pull/4283
+- Mixtral support https://github.com/ggerganov/llama.cpp/pull/4406
+- BERT embeddings https://github.com/ggerganov/llama.cpp/pull/5423
+- Grok-1 support https://github.com/ggerganov/llama.cpp/pull/6204
+- Command R Plus support https://github.com/ggerganov/llama.cpp/pull/6491
+- support arch DBRX https://github.com/ggerganov/llama.cpp/pull/6515
+- How to convert HuggingFace model to GGUF format https://github.com/ggerganov/llama.cpp/discussions/2948
--- /dev/null
+# Debugging Tests Tips
+
+## How to run & execute or debug a specific test without anything else to keep the feedback loop short?
+
+There is a script called debug-test.sh in the scripts folder whose parameter takes a REGEX and an optional test number.
+
+For example, running the following command will output an interactive list from which you can select a test. It takes this form:
+
+`debug-test.sh [OPTION]... <test_regex> <test_number>`
+
+It will then build & run in the debugger for you.
+
+To just execute a test and get back a PASS or FAIL message run:
+
+```bash
+./scripts/debug-test.sh test-tokenizer
+```
+
+To test in GDB use the `-g` flag to enable gdb test mode.
+
+```bash
+./scripts/debug-test.sh -g test-tokenizer
+
+# Once in the debugger, i.e. at the chevrons prompt, setting a breakpoint could be as follows:
+>>> b main
+```
+
+To speed up the testing loop, if you know your test number you can just run it similar to below:
+
+```bash
+./scripts/debug-test.sh test 23
+```
+
+For further reference use `debug-test.sh -h` to print help.
+
+
+
+### How does the script work?
+If you want to be able to use the concepts contained in the script separately, the important ones are briefly outlined below.
+
+#### Step 1: Reset and Setup folder context
+
+From base of this repository, let's create `build-ci-debug` as our build context.
+
+```bash
+rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug
+```
+
+#### Step 2: Setup Build Environment and Compile Test Binaries
+
+Setup and trigger a build under debug mode. You may adapt the arguments as needed, but in this case these are sane defaults.
+
+```bash
+cmake -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON ..
+make -j
+```
+
+#### Step 3: Find all tests available that matches REGEX
+
+The output of this command will give you the command & arguments needed to run GDB.
+
+* `-R test-tokenizer` : looks for all the test files named `test-tokenizer*` (R=Regex)
+* `-N` : "show-only" disables test execution & shows test commands that you can feed to GDB.
+* `-V` : Verbose Mode
+
+```bash
+ctest -R "test-tokenizer" -V -N
+```
+
+This may return output similar to below (focusing on key lines to pay attention to):
+
+```bash
+...
+1: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf"
+1: Working Directory: .
+Labels: main
+ Test #1: test-tokenizer-0-llama-spm
+...
+4: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-falcon.gguf"
+4: Working Directory: .
+Labels: main
+ Test #4: test-tokenizer-0-falcon
+...
+```
+
+#### Step 4: Identify Test Command for Debugging
+
+So for test #1 above we can tell these two pieces of relevant information:
+* Test Binary: `~/llama.cpp/build-ci-debug/bin/test-tokenizer-0`
+* Test GGUF Model: `~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf`
+
+#### Step 5: Run GDB on test command
+
+Based on the ctest 'test command' report above we can then run a gdb session via this command below:
+
+```bash
+gdb --args ${Test Binary} ${Test GGUF Model}
+```
+
+Example:
+
+```bash
+gdb --args ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf"
+```
--- /dev/null
+# Token generation performance troubleshooting
+
+## Verifying that the model is running on the GPU with CUDA
+Make sure you compiled llama with the correct env variables according to [this guide](../README.md#CUDA), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example:
+```shell
+./llama-cli -m "path/to/model.gguf" -ngl 200000 -p "Please sir, may I have some "
+```
+
+When running llama, before it starts the inference work, it will output diagnostic information that shows whether cuBLAS is offloading work to the GPU. Look for these lines:
+```shell
+llama_model_load_internal: [cublas] offloading 60 layers to GPU
+llama_model_load_internal: [cublas] offloading output layer to GPU
+llama_model_load_internal: [cublas] total VRAM used: 17223 MB
+... rest of inference
+```
+
+If you see these lines, then the GPU is being used.
+
+## Verifying that the CPU is not oversaturated
+llama accepts a `-t N` (or `--threads N`) parameter. It's extremely important that this parameter is not too large. If your token generation is extremely slow, try setting this number to 1. If this significantly improves your token generation speed, then your CPU is being oversaturated and you need to explicitly set this parameter to the number of the physical CPU cores on your machine (even if you utilize a GPU). If in doubt, start with 1 and double the amount until you hit a performance bottleneck, then scale the number down.
+
+# Example of runtime flags effect on inference speed benchmark
+These runs were tested on the following machine:
+GPU: A6000 (48GB VRAM)
+CPU: 7 physical cores
+RAM: 32GB
+
+Model: `TheBloke_Wizard-Vicuna-30B-Uncensored-GGML/Wizard-Vicuna-30B-Uncensored.q4_0.gguf` (30B parameters, 4bit quantization, GGML)
+
+Run command: `./llama-cli -m "path/to/model.gguf" -p "An extremely detailed description of the 10 best ethnic dishes will follow, with recipes: " -n 1000 [additional benchmark flags]`
+
+Result:
+
+| command | tokens/second (higher is better) |
+| - | - |
+| -ngl 2000000 | N/A (less than 0.1) |
+| -t 7 | 1.7 |
+| -t 1 -ngl 2000000 | 5.5 |
+| -t 7 -ngl 2000000 | 8.7 |
+| -t 4 -ngl 2000000 | 9.1 |
--- /dev/null
+# Docker
+
+## Prerequisites
+* Docker must be installed and running on your system.
+* Create a folder to store big models & intermediate files (ex. /llama/models)
+
+## Images
+We have three Docker images available for this project:
+
+1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`)
+2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`)
+3. `ghcr.io/ggerganov/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`)
+
+Additionally, there the following images, similar to the above:
+
+- `ghcr.io/ggerganov/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`)
+- `ghcr.io/ggerganov/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`)
+- `ghcr.io/ggerganov/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`)
+- `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
+- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
+- `ghcr.io/ggerganov/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
+
+The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
+
+## Usage
+
+The easiest way to download the models, convert them to ggml and optimize them is with the --all-in-one command which includes the full docker image.
+
+Replace `/path/to/models` below with the actual path where you downloaded the models.
+
+```bash
+docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-one "/models/" 7B
+```
+
+On completion, you are ready to play!
+
+```bash
+docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
+```
+
+or with a light image:
+
+```bash
+docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
+```
+
+or with a server image:
+
+```bash
+docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512
+```
+
+## Docker With CUDA
+
+Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container.
+
+## Building Docker locally
+
+```bash
+docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile .
+docker build -t local/llama.cpp:light-cuda -f .devops/llama-cli-cuda.Dockerfile .
+docker build -t local/llama.cpp:server-cuda -f .devops/llama-server-cuda.Dockerfile .
+```
+
+You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture.
+
+The defaults are:
+
+- `CUDA_VERSION` set to `11.7.1`
+- `CUDA_DOCKER_ARCH` set to `all`
+
+The resulting images, are essentially the same as the non-CUDA images:
+
+1. `local/llama.cpp:full-cuda`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
+2. `local/llama.cpp:light-cuda`: This image only includes the main executable file.
+3. `local/llama.cpp:server-cuda`: This image only includes the server executable file.
+
+## Usage
+
+After building locally, Usage is similar to the non-CUDA examples, but you'll need to add the `--gpus` flag. You will also want to use the `--n-gpu-layers` flag.
+
+```bash
+docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
+docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
+docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1
+```
--- /dev/null
+# Install pre-built version of llama.cpp
+
+## Homebrew
+
+On Mac and Linux, the homebrew package manager can be used via
+
+```sh
+brew install llama.cpp
+```
+The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggerganov/llama.cpp/discussions/7668
+
+## Nix
+
+On Mac and Linux, the Nix package manager can be used via
+
+```sh
+nix profile install nixpkgs#llama-cpp
+```
+For flake enabled installs.
+
+Or
+
+```sh
+nix-env --file '<nixpkgs>' --install --attr llama-cpp
+```
+
+For non-flake enabled installs.
+
+This expression is automatically updated within the [nixpkgs repo](https://github.com/NixOS/nixpkgs/blob/nixos-24.05/pkgs/by-name/ll/llama-cpp/package.nix#L164).
+
+## Flox
+
+On Mac and Linux, Flox can be used to install llama.cpp within a Flox environment via
+
+```sh
+flox install llama-cpp
+```
+
+Flox follows the nixpkgs build of llama.cpp.
+++ /dev/null
-# Token generation performance troubleshooting
-
-## Verifying that the model is running on the GPU with CUDA
-Make sure you compiled llama with the correct env variables according to [this guide](../README.md#CUDA), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example:
-```shell
-./llama-cli -m "path/to/model.gguf" -ngl 200000 -p "Please sir, may I have some "
-```
-
-When running llama, before it starts the inference work, it will output diagnostic information that shows whether cuBLAS is offloading work to the GPU. Look for these lines:
-```shell
-llama_model_load_internal: [cublas] offloading 60 layers to GPU
-llama_model_load_internal: [cublas] offloading output layer to GPU
-llama_model_load_internal: [cublas] total VRAM used: 17223 MB
-... rest of inference
-```
-
-If you see these lines, then the GPU is being used.
-
-## Verifying that the CPU is not oversaturated
-llama accepts a `-t N` (or `--threads N`) parameter. It's extremely important that this parameter is not too large. If your token generation is extremely slow, try setting this number to 1. If this significantly improves your token generation speed, then your CPU is being oversaturated and you need to explicitly set this parameter to the number of the physical CPU cores on your machine (even if you utilize a GPU). If in doubt, start with 1 and double the amount until you hit a performance bottleneck, then scale the number down.
-
-# Example of runtime flags effect on inference speed benchmark
-These runs were tested on the following machine:
-GPU: A6000 (48GB VRAM)
-CPU: 7 physical cores
-RAM: 32GB
-
-Model: `TheBloke_Wizard-Vicuna-30B-Uncensored-GGML/Wizard-Vicuna-30B-Uncensored.q4_0.gguf` (30B parameters, 4bit quantization, GGML)
-
-Run command: `./llama-cli -m "path/to/model.gguf" -p "An extremely detailed description of the 10 best ethnic dishes will follow, with recipes: " -n 1000 [additional benchmark flags]`
-
-Result:
-
-| command | tokens/second (higher is better) |
-| - | - |
-| -ngl 2000000 | N/A (less than 0.1) |
-| -t 7 | 1.7 |
-| -t 1 -ngl 2000000 | 5.5 |
-| -t 7 -ngl 2000000 | 8.7 |
-| -t 4 -ngl 2000000 | 9.1 |
Note: It is synced from llama.cpp `main` every 6 hours.
-## Llama 2 7B
+Example usage:
+
+```bash
+# obtain the official LLaMA model weights and place them in ./models
+ls ./models
+llama-2-7b tokenizer_checklist.chk tokenizer.model
+# [Optional] for models using BPE tokenizers
+ls ./models
+<folder containing weights and tokenizer json> vocab.json
+# [Optional] for PyTorch .bin models like Mistral-7B
+ls ./models
+<folder containing weights and tokenizer json>
+
+# install Python dependencies
+python3 -m pip install -r requirements.txt
+
+# convert the model to ggml FP16 format
+python3 convert_hf_to_gguf.py models/mymodel/
+
+# quantize the model to 4-bits (using Q4_K_M method)
+./llama-quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M
+
+# update the gguf filetype to current version if older version is now unsupported
+./llama-quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY
+```
+
+Run the quantized model:
+
+```bash
+# start inference on a gguf model
+./llama-cli -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128
+```
+
+When running the larger models, make sure you have enough disk space to store all the intermediate files.
+
+## Memory/Disk Requirements
+
+As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same.
+
+| Model | Original size | Quantized size (Q4_0) |
+|------:|--------------:|----------------------:|
+| 7B | 13 GB | 3.9 GB |
+| 13B | 24 GB | 7.8 GB |
+| 30B | 60 GB | 19.5 GB |
+| 65B | 120 GB | 38.5 GB |
+
+## Quantization
+
+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 |
+| 7B | file size | 13.0G | 3.5G | 3.9G | 4.3G | 4.7G | 6.7G |
+| 7B | ms/tok @ 4th | 127 | 55 | 54 | 76 | 83 | 72 |
+| 7B | ms/tok @ 8th | 122 | 43 | 45 | 52 | 56 | 67 |
+| 7B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 |
+| 13B | perplexity | 5.2543 | 5.3860 | 5.3608 | 5.2856 | 5.2706 | 5.2548 |
+| 13B | file size | 25.0G | 6.8G | 7.6G | 8.3G | 9.1G | 13G |
+| 13B | ms/tok @ 4th | - | 103 | 105 | 148 | 160 | 131 |
+| 13B | ms/tok @ 8th | - | 73 | 82 | 98 | 105 | 128 |
+| 13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 |
+
+- [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684)
+- recent k-quants improvements and new i-quants
+ - [#2707](https://github.com/ggerganov/llama.cpp/pull/2707)
+ - [#2807](https://github.com/ggerganov/llama.cpp/pull/2807)
+ - [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773)
+ - [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856)
+ - [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861)
+ - [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872)
+ - [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897)
+ - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930)
+ - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957)
+ - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969)
+ - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996)
+ - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060)
+ - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196)
+ - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361)
+
+**Llama 2 7B**
| Quantization | Bits per Weight (BPW) |
|--------------|-----------------------|
| Q5_K_M | 5.68 |
| Q6_K | 6.56 |
-## Llama 2 13B
+**Llama 2 13B**
+
Quantization | Bits per Weight (BPW)
-- | --
Q2_K | 3.34
Q5_K_M | 5.67
Q6_K | 6.56
-# Llama 2 70B
+**Llama 2 70B**
Quantization | Bits per Weight (BPW)
-- | --
overview / pkg / ggml / sources / llama.cpp / commitdiff