llama.cpp

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Inference of LLaMA model in pure C/C++

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Table of Contents

Description
Usage
Contributing
Coding guidelines
Docs

Description

The main goal of llama.cpp is to run the LLaMA model using 4-bit integer quantization on a MacBook

Plain C/C++ implementation without dependencies
Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
AVX, AVX2 and AVX512 support for x86 architectures
Mixed F16 / F32 precision
2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quantization support
CUDA, Metal and OpenCL GPU backend support

The original implementation of llama.cpp was hacked in an evening. Since then, the project has improved significantly thanks to many contributions. This project is mainly for educational purposes and serves as the main playground for developing new features for the ggml library.

Supported platforms:

Mac OS
Linux
Windows (via CMake)
Docker

Supported models:

Bindings:

UI:

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

$ make -j && ./main -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e
I llama.cpp build info:
I UNAME_S:  Darwin
I UNAME_P:  arm
I UNAME_M:  arm64
I CFLAGS:   -I.            -O3 -std=c11   -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -pthread -DGGML_USE_K_QUANTS -DGGML_USE_ACCELERATE
I CXXFLAGS: -I. -I./common -O3 -std=c++11 -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar -pthread -DGGML_USE_K_QUANTS
I LDFLAGS:   -framework Accelerate
I CC:       Apple clang version 14.0.3 (clang-1403.0.22.14.1)
I CXX:      Apple clang version 14.0.3 (clang-1403.0.22.14.1)

make: Nothing to be done for `default'.
main: build = 1041 (cf658ad)
main: seed  = 1692823051
llama_model_loader: loaded meta data with 16 key-value pairs and 363 tensors from models/llama-13b-v2/ggml-model-q4_0.gguf (version GGUF V1 (latest))
llama_model_loader: - type  f32:   81 tensors
llama_model_loader: - type q4_0:  281 tensors
llama_model_loader: - type q6_K:    1 tensors
llm_load_print_meta: format         = GGUF V1 (latest)
llm_load_print_meta: arch           = llama
llm_load_print_meta: vocab type     = SPM
llm_load_print_meta: n_vocab        = 32000
llm_load_print_meta: n_merges       = 0
llm_load_print_meta: n_ctx_train    = 4096
llm_load_print_meta: n_ctx          = 512
llm_load_print_meta: n_embd         = 5120
llm_load_print_meta: n_head         = 40
llm_load_print_meta: n_head_kv      = 40
llm_load_print_meta: n_layer        = 40
llm_load_print_meta: n_rot          = 128
llm_load_print_meta: n_gqa          = 1
llm_load_print_meta: f_norm_eps     = 1.0e-05
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: n_ff           = 13824
llm_load_print_meta: freq_base      = 10000.0
llm_load_print_meta: freq_scale     = 1
llm_load_print_meta: model type     = 13B
llm_load_print_meta: model ftype    = mostly Q4_0
llm_load_print_meta: model size     = 13.02 B
llm_load_print_meta: general.name   = LLaMA v2
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token  = 13 '<0x0A>'
llm_load_tensors: ggml ctx size =    0.11 MB
llm_load_tensors: mem required  = 7024.01 MB (+  400.00 MB per state)
...................................................................................................
llama_new_context_with_model: kv self size  =  400.00 MB
llama_new_context_with_model: compute buffer total size =   75.41 MB

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


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

And here is another demo of running both LLaMA-7B and whisper.cpp on a single M1 Pro MacBook:

whisper-llama-lq.mp4

Usage

Here are the end-to-end binary build and model conversion steps for the LLaMA-7B model.

Get the Code

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp

Build

In order to build llama.cpp you have three different options.

Using make:
- On Linux or MacOS:
```
make
```
- On Windows:
  1. Download the latest fortran version of w64devkit.
  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:
```
make
```

Using CMake:

mkdir build
cd build
cmake ..
cmake --build . --config Release

Using Zig (version 0.11 or later):

Building for optimization levels and CPU features can be accomplished using standard build arguments, for example AVX2, FMA, F16C, it's also possible to cross compile for other operating systems and architectures:
```
zig build -Doptimize=ReleaseFast -Dtarget=x86_64-windows-gnu -Dcpu=x86_64+avx2+fma+f16c
```
The zig targets command will give you valid options to use.
Using gmake (FreeBSD):
1. Install and activate DRM in FreeBSD
2. Add your user to video group
3. Install compilation dependencies.
```
sudo pkg install gmake automake autoconf pkgconf llvm15 clinfo clover \
    opencl clblast openblas

    gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4
```
Notes: With this packages you can build llama.cpp with OPENBLAS and CLBLAST support for use OpenCL GPU acceleration in FreeBSD. Please read the instructions for use and activate this options in this document below.

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 LLAMA_NO_METAL=1 flag or the LLAMA_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.

MPI Build

MPI lets you distribute the computation over a cluster of machines. Because of the serial nature of LLM prediction, this won't yield any end-to-end speed-ups, but it will let you run larger models than would otherwise fit into RAM on a single machine.

First you will need MPI libraries installed on your system. The two most popular (only?) options are MPICH and OpenMPI. Either can be installed with a package manager (apt, Homebrew, MacPorts, etc).

Next you will need to build the project with LLAMA_MPI set to true on all machines; if you're building with make, you will also need to specify an MPI-capable compiler (when building with CMake, this is configured automatically):

Using make:
```
make CC=mpicc CXX=mpicxx LLAMA_MPI=1
```
Using CMake:
```
cmake -S . -B build -DLLAMA_MPI=ON
```

Once the programs are built, download/convert the weights on all of the machines in your cluster. The paths to the weights and programs should be identical on all machines.

Next, ensure password-less SSH access to each machine from the primary host, and create a hostfile with a list of the hostnames and their relative "weights" (slots). If you want to use localhost for computation, use its local subnet IP address rather than the loopback address or "localhost".

Here is an example hostfile:

192.168.0.1:2
malvolio.local:1

The above will distribute the computation across 2 processes on the first host and 1 process on the second host. Each process will use roughly an equal amount of RAM. Try to keep these numbers small, as inter-process (intra-host) communication is expensive.

Finally, you're ready to run a computation using mpirun:

mpirun -hostfile hostfile -n 3 ./main -m ./models/7B/ggml-model-q4_0.gguf -n 128

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). BLAS doesn't affect the normal generation performance. There are currently three different implementations of it:

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:
```
make LLAMA_OPENBLAS=1
```
  - On Windows:
    1. Download the latest fortran version of w64devkit.
    2. Download the latest version of OpenBLAS for Windows.
    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:
      make LLAMA_OPENBLAS=1
- Using CMake on Linux:
```
mkdir build
cd build
cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
cmake --build . --config Release
```
BLIS

Check BLIS.md for more information.
Intel MKL

By default, LLAMA_BLAS_VENDOR is set to Generic, so if you already sourced intel environment script and assign -DLLAMA_BLAS=ON in cmake, the mkl version of Blas will automatically been selected. You may also specify it by:
```
mkdir build
cd build
cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
cmake --build . --config Release
```
cuBLAS

This provides BLAS 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.
- Using make:
```
make LLAMA_CUBLAS=1
```
- Using CMake:
```
mkdir build
cd build
cmake .. -DLLAMA_CUBLAS=ON
cmake --build . --config Release
```
The environment variable CUDA_VISIBLE_DEVICES 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
LLAMA_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.
LLAMA_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.
LLAMA_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 recomm 1C6A ended.
LLAMA_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.
LLAMA_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.
LLAMA_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.

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).

Using make:
```
make LLAMA_HIPBLAS=1
```

Using CMake for Linux:

mkdir build
cd build
CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ cmake .. -DLLAMA_HIPBLAS=ON
cmake --build .

Using CMake for Windows:
```
mkdir build
cd build
cmake -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ ..
cmake --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

The environment variable 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 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
LLAMA_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.
LLAMA_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.
LLAMA_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.

CLBlast

OpenCL acceleration is provided by the matrix multiplication kernels from the CLBlast project and custom kernels for ggml that can generate tokens on the GPU.

You will need the OpenCL SDK.

For Ubuntu or Debian, the packages opencl-headers, ocl-icd may be needed.
For Windows, a pre-built SDK is available on the OpenCL Releases page.

Installing the OpenCL SDK from source

git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git
mkdir OpenCL-SDK/build
cd OpenCL-SDK/build
cmake .. -DBUILD_DOCS=OFF \
  -DBUILD_EXAMPLES=OFF \
  -DBUILD_TESTING=OFF \
  -DOPENCL_SDK_BUILD_SAMPLES=OFF \
  -DOPENCL_SDK_TEST_SAMPLES=OFF
cmake --build . --config Release
cmake --install . --prefix /some/path

Installing CLBlast

Pre-built CLBlast binaries may be found on the CLBlast Releases page. For Unix variants, it may also be found in your operating system's packages.

Alternatively, they may be built from source.

Windows:

set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64"
git clone https://github.com/CNugteren/CLBlast.git
mkdir CLBlast\build
cd CLBlast\build
cmake .. -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64
cmake --build . --config Release
cmake --install . --prefix C:/CLBlast

Unix:

git clone https://github.com/CNugteren/CLBlast.git
mkdir CLBlast/build
cd CLBlast/build
cmake .. -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
cmake --build . --config Release
cmake --install . --prefix /some/path

Where /some/path is where the built library will be installed (default is /usr/local).

Building Llama with CLBlast

Build with make:
```
make LLAMA_CLBLAST=1
```

CMake (Unix):

mkdir build
cd build
cmake .. -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path
cmake --build . --config Release

CMake (Windows):

set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast"
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
mkdir build
cd build
cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64
cmake --build . --config Release
cmake --install . --prefix C:/LlamaCPP

Running Llama with CLBlast

The CLBlast build supports --gpu-layers|-ngl like the CUDA version does.

To select the correct platform (driver) and device (GPU), you can use the environment variables GGML_OPENCL_PLATFORM and GGML_OPENCL_DEVICE. The selection can be a number (starting from 0) or a text string to search:

GGML_OPENCL_PLATFORM=1 ./main ...
GGML_OPENCL_DEVICE=2 ./main ...
GGML_OPENCL_PLATFORM=Intel ./main ...
GGML_OPENCL_PLATFORM=AMD GGML_OPENCL_DEVICE=1 ./main ...

The default behavior is to find the first GPU device, but when it is an integrated GPU on a laptop, for instance, the selectors are useful. Using the variables it is possible to select a CPU-based driver as well, if so desired.

You can get a list of platforms and devices from the clinfo -l command, etc.

Prepare Data & Run

# obtain the original LLaMA model weights and place them in ./models
ls ./models
65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model
  # [Optional] for models using BPE tokenizers
  ls ./models
  65B 30B 13B 7B vocab.json

# install Python dependencies
python3 -m pip install -r requirements.txt

# convert the 7B model to ggml FP16 format
python3 convert.py models/7B/

  # [Optional] for models using BPE tokenizers
  python convert.py models/7B/ --vocabtype bpe

# quantize the model to 4-bits (using q4_0 method)
./quantize ./models/7B/ggml-model-f16.gguf ./models/7B/ggml-model-q4_0.gguf q4_0

# update the gguf filetype to current if older version is unsupported by another application
./quantize ./models/7B/ggml-model-q4_0.gguf ./models/7B/ggml-model-q4_0-v2.gguf COPY


# run the inference
./main -m ./models/7B/ggml-model-q4_0.gguf -n 128

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.

Simply download and extract the latest zip package of choice: (e.g. llama-b1380-bin-win-avx2-x64.zip)

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:

.\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 (4-bit)
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
recent k-quants improvements
- #2707
- #2807

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.

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

Download/extract: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
Run ./perplexity -m models/7B/ggml-model-q4_0.gguf -f wiki.test.raw
Output:

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. 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

# default arguments using a 7B model
./examples/chat.sh

# advanced chat with a 13B model
./examples/chat-13B.sh

# custom arguments using a 13B model
./main -m ./models/13B/ggml-model-q4_0.gguf -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt

Note the use of --color to distinguish between user input and generated text. Other parameters are explained in more detail in the README for the main example program.

Persistent Interaction

The prompt, user inputs, and model generations can be saved and resumed across calls to ./main by leveraging --prompt-cache and --prompt-cache-all. The ./examples/chat-persistent.sh script demonstrates this with support for long-running, resumable chat sessions. To use this example, you must provide a file to cache the initial chat prompt and a directory to save the chat session, and may optionally provide the same variables as chat-13B.sh. The same prompt cache can be reused for new chat sessions. Note that both prompt cache and chat directory are tied to the initial prompt (PROMPT_TEMPLATE) and the model file.

# Start a new chat
PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh

# Resume that chat
PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh

# Start a different chat with the same prompt/model
PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/another ./examples/chat-persistent.sh

# Different prompt cache for different prompt/model
PROMPT_TEMPLATE=./prompts/chat-with-bob.txt PROMPT_CACHE_FILE=bob.prompt.bin \
    CHAT_SAVE_DIR=./chat/bob ./examples/chat-persistent.sh

Constrained output with grammars

llama.cpp supports grammars to constrain model output. For example, you can force the model to output JSON only:

./main -m ./models/13B/ggml-model-q4_0.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'

The grammars/ folder contains a handful of sample grammars. To write your own, check out the GBNF Guide.

For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on its repo and not this one.

Instruction mode with Alpaca

First, download the ggml Alpaca model into the ./models folder
Run the main tool like this:

./examples/alpaca.sh

Sample run:

== Running in interactive mode. ==
 - Press Ctrl+C to interject at any time.
 - Press Return to return control to LLaMa.
 - If you want to submit another line, end your input in '\'.

 Below is an instruction that describes a task. Write a response that appropriately completes the request.

> How many letters are there in the English alphabet?
There 26 letters in the English Alphabet
> What is the most common way of transportation in Amsterdam?
The majority (54%) are using public transit. This includes buses, trams and metros with over 100 lines throughout the city which make it very accessible for tourists to navigate around town as well as locals who commute by tram or metro on a daily basis
> List 5 words that start with "ca".
cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach.
>

Using OpenLLaMA

OpenLLaMA is an openly licensed reproduction of Meta's original LLaMA model. It uses the same architecture and is a drop-in replacement for the original LLaMA weights.

Download the 3B, 7B, or 13B model from Hugging Face.
Convert the model to ggml FP16 format using python convert.py <path to OpenLLaMA directory>

Using GPT4All

Note: these instructions are likely obsoleted by the GGUF update

Obtain the tokenizer.model file from LLaMA model and put it to models
Obtain the added_tokens.json file from Alpaca model and put it to models
Obtain the gpt4all-lora-quantized.bin file from GPT4All model and put it to models/gpt4all-7B
It is distributed in the old ggml format which is now obsoleted
You have to convert it to the new format using convert.py:

python3 convert.py models/gpt4all-7B/gpt4all-lora-quantized.bin

You can now use the newly generated models/gpt4all-7B/ggml-model-q4_0.bin model in exactly the same way as all other models
The newer GPT4All-J model is not yet supported!

Using Pygmalion 7B & Metharme 7B

Obtain the LLaMA weights
Obtain the Pygmalion 7B or Metharme 7B XOR encoded weights
Convert the LLaMA model with the latest HF convert script
Merge the XOR files with the converted LLaMA weights by running the xor_codec script
Convert to ggml format using the convert.py script in this repo:

python3 convert.py pygmalion-7b/ --outtype q4_1

The Pygmalion 7B & Metharme 7B weights are saved in bfloat16 precision. If you wish to convert to ggml without quantizating, please specify the --outtype as f32 instead of f16.

Obtaining the Facebook LLaMA original model and Stanford Alpaca model data

Under no circumstances should IPFS, magnet links, or any other links to model downloads be shared anywhere in this repository, including in issues, discussions, or pull requests. They will be immediately deleted.
The LLaMA models are officially distributed by Facebook and will never be provided through this repository.
Refer to Facebook's LLaMA repository if you need to request access to the model data.

Obtaining and using the Facebook LLaMA 2 model

Refer to Facebook's LLaMA download page if you want to access the model data.
Alternatively, if you want to save time and space, you can download already converted and quantized models from TheBloke, including:

Verifying the model files

Please verify the sha256 checksums of all downloaded model files to confirm that you have the correct model data files before creating an issue relating to your model files.

The following python script will verify if you have all possible latest files in your self-installed ./models subdirectory:

# run the verification script
./scripts/verify-checksum-models.py

On linux or macOS it is also possible to run the following commands to verify if you have all possible latest files in your self-installed ./models subdirectory:
- On Linux: sha256sum --ignore-missing -c SHA256SUMS
- on macOS: shasum -a 256 --ignore-missing -c SHA256SUMS

Seminal papers and background on the models

If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:

LLaMA:
- Introducing LLaMA: A foundational, 65-billion-parameter large language model
- LLaMA: Open and Efficient Foundation Language Models
GPT-3
- Language Models are Few-Shot Learners
GPT-3.5 / InstructGPT / ChatGPT:
- Aligning language models to follow instructions
- Training language models to follow instructions with human feedback

Android

Building the Project using Android NDK

You can easily run llama.cpp on Android device with termux.

First, install the essential packages for termux:

pkg install clang wget git cmake

Second, obtain the Android NDK and then build with CMake:

$ mkdir build-android
$ cd build-android
$ export NDK=<your_ndk_directory>
$ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
$ make

Install termux on your device and run termux-setup-storage to get access to your SD card. Finally, copy the llama binary and the model files to your device storage. Here is a demo of an interactive session running on Pixel 5 phone:

llama-interactive2.mp4

Building the Project using Termux (F-Droid)

Termux from F-Droid offers an alternative route to execute the project on an Android device. This method empowers you to construct the project right from within the terminal, negating the requirement for a rooted device or SD Card.

Outlined below are the directives for installing the project using OpenBLAS and CLBlast. This combination is specifically designed to deliver peak performance on recent devices that feature a GPU.

If you opt to utilize OpenBLAS, you'll need to install the corresponding package.

apt install libopenblas

Subsequently, if you decide to incorporate CLBlast, you'll first need to install the requisite OpenCL packages:

apt install ocl-icd opencl-headers opencl-clhpp clinfo

In order to compile CLBlast, you'll need to first clone the respective Git repository, which can be found at this URL: https://github.com/CNugteren/CLBlast. Alongside this, clone this repository into your home directory. Once this is done, navigate to the CLBlast folder and execute the commands detailed below:

cmake .
make
cp libclblast.so* $PREFIX/lib
cp ./include/clblast.h ../llama.cpp

Following the previous steps, navigate to the LlamaCpp directory. To compile it with OpenBLAS and CLBlast, execute the command provided below:

cp /data/data/com.termux/files/usr/include/openblas/cblas.h .
cp /data/data/com.termux/files/usr/include/openblas/openblas_config.h .
make LLAMA_CLBLAST=1 //(sometimes you need to run this command twice)

Upon completion of the aforementioned steps, you will have successfully compiled the project. To run it using CLBlast, a slight adjustment is required: a command must be issued to direct the operations towards your device's physical GPU, rather than the virtual one. The necessary command is detailed below:

GGML_OPENCL_PLATFORM=0
GGML_OPENCL_DEVICE=0
export LD_LIBRARY_PATH=/vendor/lib64:$LD_LIBRARY_PATH

(Note: some Android devices, like the Zenfone 8, need the following command instead - "export LD_LIBRARY_PATH=/system/vendor/lib64:$LD_LIBRARY_PATH". Source: https://www.reddit.com/r/termux/comments/kc3ynp/opencl_working_in_termux_more_in_comments/ )

For easy and swift re-execution, consider documenting this final part in a .sh script file. This will enable you to rerun the process with minimal hassle.

Place your desired model into the ~/llama.cpp/models/ directory and execute the ./main (...) script.

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 two Docker images available for this project:

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)
ghcr.io/ggerganov/llama.cpp:light: This image only includes the main 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: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)

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/ and the Gitlab Action defined in .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.

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!

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:

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

Docker With CUDA

Assuming one has the nvidia-container-toolkit properly installed on Linux, or is using a GPU enabled cloud, cuBLAS should be accessible inside the container.

Building Locally

docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile .
docker build -t local/llama.cpp:light-cuda -f .devops/main-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:

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.
local/llama.cpp:light-cuda: This image only includes the main 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.

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

Contributing

Contributors can open PRs
Collaborators can push to branches in the llama.cpp repo and merge PRs into the master branch
Collaborators will be invited based on contributions
Any help with managing issues and PRs is very appreciated!
Make sure to read this: Inference at the edge
A bit of backstory for those who are interested: Changelog podcast

Coding guidelines

Avoid adding third-party dependencies, extra files, extra headers, etc.
Always consider cross-compatibility with other operating systems and architectures
Avoid fancy looking modern STL constructs, use basic for loops, avoid templates, keep it simple
There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit
Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, void * ptr, int & a
See good first issues for tasks suitable for first contributions

Name		Name	Last commit message	Last commit date
Latest commit History 1,562 Commits
.devops		.devops
.github		.github
ci		ci
cmake		cmake
common		common
docs		docs
examples		examples
gguf-py		gguf-py
grammars		grammars
media		media
models		models
pocs		pocs
prompts		prompts
scripts		scripts
spm-headers		spm-headers
tests		tests
.clang-tidy		.clang-tidy
.dockerignore		.dockerignore
.ecrc		.ecrc
.editorconfig		.editorconfig
.flake8		.flake8
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
CMakeLists.txt		CMakeLists.txt
LICENSE		LICENSE
Makefile		Makefile
Package.swift		Package.swift
README.md		README.md
SHA256SUMS		SHA256SUMS
build.zig		build.zig
codecov.yml		codecov.yml
convert-hf-to-gguf.py		convert-hf-to-gguf.py
convert-llama-ggml-to-gguf.py		convert-llama-ggml-to-gguf.py
convert-lora-to-ggml.py		convert-lora-to-ggml.py
convert-persimmon-to-gguf.py		convert-persimmon-to-gguf.py
convert.py		convert.py
flake.lock		flake.lock
flake.nix		flake.nix
ggml-alloc.c		ggml-alloc.c
ggml-alloc.h		ggml-alloc.h
ggml-backend-impl.h		ggml-backend-impl.h
ggml-backend.c		ggml-backend.c
ggml-backend.h		ggml-backend.h
ggml-cuda.cu		ggml-cuda.cu
ggml-cuda.h		ggml-cuda.h
ggml-impl.h		ggml-impl.h
ggml-metal.h		ggml-metal.h
ggml-metal.m		ggml-metal.m
ggml-metal.metal		ggml-metal.metal
ggml-mpi.c		ggml-mpi.c
ggml-mpi.h		ggml-mpi.h
ggml-opencl.cpp		ggml-opencl.cpp
ggml-opencl.h		ggml-opencl.h
ggml-quants.c		ggml-quants.c
ggml-quants.h		ggml-quants.h
ggml.c		ggml.c
ggml.h		ggml.h
llama.cpp		llama.cpp
llama.h		llama.h
mypy.ini		mypy.ini
requirements.txt		requirements.txt
run_with_preset.py		run_with_preset.py
unicode.h		unicode.h

License

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llama.cpp

Hot topics

Description

Usage

Get the Code

Build

Metal Build

MPI Build

BLAS Build

Accelerate Framework:

OpenBLAS:

BLIS

Intel MKL

cuBLAS

hipBLAS

CLBlast

Installing CLBlast

Building Llama with CLBlast

Running Llama with CLBlast

Prepare Data & Run

Running on Windows with prebuilt binaries

Memory/Disk Requirements

Quantization

Perplexity (measuring model quality)

How to run

Interactive mode

Persistent Interaction

Constrained output with grammars

Instruction mode with Alpaca

Using OpenLLaMA

Using GPT4All

Using Pygmalion 7B & Metharme 7B

Obtaining the Facebook LLaMA original model and Stanford Alpaca model data

Obtaining and using the Facebook LLaMA 2 model

Verifying the model files

Seminal papers and background on the models

Android

Building the Project using Android NDK

Building the Project using Termux (F-Droid)

Docker

Prerequisites

Images

Usage

Docker With CUDA

Building Locally

Usage

Contributing

Coding guidelines

Docs

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