# llama.cpp ![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png) [![Actions Status](https://github.com/ggerganov/llama.cpp/workflows/CI/badge.svg)](https://github.com/ggerganov/llama.cpp/actions) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ **Hot topics:** - [Roadmap May 2023](https://github.com/ggerganov/llama.cpp/discussions/1220) - [New quantization methods](https://github.com/ggerganov/llama.cpp#quantization)
Table of Contents
  1. Description
  2. Usage
  3. Contributing
  4. Coding guidelines
  5. 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 and Accelerate framework - AVX, AVX2 and AVX512 support for x86 architectures - Mixed F16 / F32 precision - 4-bit, 5-bit and 8-bit integer quantization support - Runs on the CPU - OpenBLAS support - cuBLAS and CLBlast support The original implementation of `llama.cpp` was [hacked in an evening](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022). Since then, the project has improved significantly thanks to many contributions. This project is for educational purposes and serves as 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 **Supported models:** - [X] LLaMA šŸ¦™ - [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca) - [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all) - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne) - [X] [Vicuna](https://github.com/ggerganov/llama.cpp/discussions/643#discussioncomment-5533894) - [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/) - [X] [OpenBuddy šŸ¶ (Multilingual)](https://github.com/OpenBuddy/OpenBuddy) - [X] [Pygmalion 7B / Metharme 7B](#using-pygmalion-7b--metharme-7b) **Bindings:** - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python) - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp) - Node.js: [hlhr202/llama-node](https://github.com/hlhr202/llama-node) - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb) **UI:** - [nat/openplayground](https://github.com/nat/openplayground) - [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) --- Here is a typical run using LLaMA-7B: ```java make -j && ./main -m ./models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -n 512 I llama.cpp build info: I UNAME_S: Darwin I UNAME_P: arm I UNAME_M: arm64 I CFLAGS: -I. -O3 -DNDEBUG -std=c11 -fPIC -pthread -DGGML_USE_ACCELERATE I CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC -pthread I LDFLAGS: -framework Accelerate I CC: Apple clang version 14.0.0 (clang-1400.0.29.202) I CXX: Apple clang version 14.0.0 (clang-1400.0.29.202) make: Nothing to be done for `default'. main: seed = 1678486056 llama_model_load: loading model from './models/7B/ggml-model-q4_0.bin' - please wait ... llama_model_load: n_vocab = 32000 llama_model_load: n_ctx = 512 llama_model_load: n_embd = 4096 llama_model_load: n_mult = 256 llama_model_load: n_head = 32 llama_model_load: n_layer = 32 llama_model_load: n_rot = 128 llama_model_load: f16 = 2 llama_model_load: n_ff = 11008 llama_model_load: ggml ctx size = 4529.34 MB llama_model_load: memory_size = 512.00 MB, n_mem = 16384 llama_model_load: .................................... done llama_model_load: model size = 4017.27 MB / num tensors = 291 main: prompt: 'Building a website can be done in 10 simple steps:' main: number of tokens in prompt = 15 1 -> '' 8893 -> 'Build' 292 -> 'ing' 263 -> ' a' 4700 -> ' website' 508 -> ' can' 367 -> ' be' 2309 -> ' done' 297 -> ' in' 29871 -> ' ' 29896 -> '1' 29900 -> '0' 2560 -> ' simple' 6576 -> ' steps' 29901 -> ':' sampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000 Building a website can be done in 10 simple steps: 1) Select a domain name and web hosting plan 2) Complete a sitemap 3) List your products 4) Write product descriptions 5) Create a user account 6) Build the template 7) Start building the website 8) Advertise the website 9) Provide email support 10) Submit the website to search engines A website is a collection of web pages that are formatted with HTML. HTML is the code that defines what the website looks like and how it behaves. The HTML code is formatted into a template or a format. Once this is done, it is displayed on the user's browser. The web pages are stored in a web server. The web server is also called a host. When the website is accessed, it is retrieved from the server and displayed on the user's computer. A website is known as a website when it is hosted. This means that it is displayed on a host. The host is usually a web server. A website can be displayed on different browsers. The browsers are basically the software that renders the website on the user's screen. A website can also be viewed on different devices such as desktops, tablets and smartphones. Hence, to have a website displayed on a browser, the website must be hosted. A domain name is an address of a website. It is the name of the website. The website is known as a website when it is hosted. This means that it is displayed on a host. The host is usually a web server. A website can be displayed on different browsers. The browsers are basically the software that renders the website on the userā€™s screen. A website can also be viewed on different devices such as desktops, tablets and smartphones. Hence, to have a website displayed on a browser, the website must be hosted. A domain name is an address of a website. It is the name of the website. A website is an address of a website. It is a collection of web pages that are formatted with HTML. HTML is the code that defines what the website looks like and how it behaves. The HTML code is formatted into a template or a format. Once this is done, it is displayed on the userā€™s browser. A website is known as a website when it is hosted main: mem per token = 14434244 bytes main: load time = 1332.48 ms main: sample time = 1081.40 ms main: predict time = 31378.77 ms / 61.41 ms per token main: total time = 34036.74 ms ``` And here is another demo of running both LLaMA-7B and [whisper.cpp](https://github.com/ggerganov/whisper.cpp) on a single M1 Pro MacBook: https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8b4f-add84093ffff.mp4 ## Usage Here are the steps for the LLaMA-7B model. ### Get the Code ```bash 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: ```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 ``` - Using `CMake`: ```bash mkdir build cd build cmake .. cmake --build . --config Release ``` - Using `Zig`: ```bash zig build -Drelease-fast ``` ### 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: ```bash make LLAMA_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 LLAMA_OPENBLAS=1 ``` - Using `CMake` on Linux: ```bash mkdir build cd build cmake .. -DLLAMA_OPENBLAS=ON 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 or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). - Using `make`: ```bash make LLAMA_CUBLAS=1 ``` - Using `CMake`: ```bash mkdir build cd build cmake .. -DLLAMA_CUBLAS=ON cmake --build . --config Release ``` Note: Because llama.cpp uses multiple CUDA streams for matrix multiplication results [are not guaranteed to be reproducible](https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility). If you need reproducibility, set `GGML_CUDA_MAX_STREAMS` in the file `ggml-cuda.cu` to 1. ### Prepare Data & Run ```bash # obtain the original LLaMA model weights and place them in ./models ls ./models 65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model # install Python dependencies python3 -m pip install -r requirements.txt # convert the 7B model to ggml FP16 format python3 convert.py models/7B/ # quantize the model to 4-bits (using q4_0 method) ./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin q4_0 # run the inference ./main -m ./models/7B/ggml-model-q4_0.bin -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 (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. | Model | Measure | F16 | Q4_0 | Q4_1 | Q4_2 | Q5_0 | Q5_1 | Q8_0 | |------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:|-------:| | 7B | perplexity | 5.9066 | 6.1620 | 6.0910 | 6.1466 | 5.9862 | 5.9481 | 5.9069 | | 7B | file size | 13.0G | 4.0G | 4.8G | 4.0G | 4.4G | 4.8G | 7.1G | | 7B | ms/tok @ 4th | 128 | 56 | 61 | 84 | 91 | 95 | 75 | | 7B | ms/tok @ 8th | 128 | 47 | 55 | 48 | 53 | 59 | 75 | | 7B | bits/weight | 16.0 | 5.0 | 6.0 | 5.0 | 5.5 | 6.0 | 9.0 | | 13B | perplexity | 5.2543 | 5.3863 | 5.3607 | 5.3513 | 5.2856 | 5.2706 | 5.2548 | | 13B | file size | 25.0G | 7.6G | 9.1G | 7.6G | 8.4G | 9.1G | 14G | | 13B | ms/tok @ 4th | 239 | 104 | 113 | 160 | 176 | 185 | 141 | | 13B | ms/tok @ 8th | 240 | 85 | 99 | 97 | 108 | 117 | 147 | | 13B | bits/weight | 16.0 | 5.0 | 6.0 | 5.0 | 5.5 | 6.0 | 9.0 | ### 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. ### 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 ```bash # 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.bin -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](examples/main/README.md) for the `main` example program. ![image](https://user-images.githubusercontent.com/1991296/224575029-2af3c7dc-5a65-4f64-a6bb-517a532aea38.png) ### Instruction mode with Alpaca 1. First, download the `ggml` Alpaca model into the `./models` folder 2. 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 [GPT4All](https://github.com/nomic-ai/gpt4all) - 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`: ```bash 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](#obtaining-the-facebook-llama-original-model-and-stanford-alpaca-model-data) - Obtain the [Pygmalion 7B](https://huggingface.co/PygmalionAI/pygmalion-7b/) or [Metharme 7B](https://huggingface.co/PygmalionAI/metharme-7b) XOR encoded weights - Convert the LLaMA model with [the latest HF convert script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py) - Merge the XOR files with the converted LLaMA weights by running the [xor_codec](https://huggingface.co/PygmalionAI/pygmalion-7b/blob/main/xor_codec.py) script - Convert to `ggml` format using the `convert.py` script in this repo: ```bash python3 convert.py pygmalion-7b/ --outtype q4_1 ``` > The Pygmalion 7B & Metharme 7B weights are saved in [bfloat16](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format) 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](https://github.com/facebookresearch/llama/pull/73/files) if you need to request access to the model data. ### Verifying the model files Please verify the [sha256 checksums](SHA256SUMS) 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: ```bash # run the verification script python3 .\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](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) - [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971) - GPT-3 - [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) - GPT-3.5 / InstructGPT / ChatGPT: - [Aligning language models to follow instructions](https://openai.com/research/instruction-following) - [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155) #### How to run 1. Download/extract: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research 2. Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw` 3. 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. ### Android You can easily run `llama.cpp` on Android device with [termux](https://termux.dev/). First, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake: ``` $ mkdir build-android $ cd build-android $ export NDK= $ 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://termux.dev/) on your device and run `termux-setup-storage` to get access to your SD card. Finally, copy the `llama` binary and the model files to your device storage. Here is a demo of an interactive session running on Pixel 5 phone: https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4 ### 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: 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. 2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. #### 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.bin -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.bin -p "Building a website can be done in 10 simple steps:" -n 512 ``` ### 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](https://github.com/ggerganov/llama.cpp/discussions/205) - A bit of backstory for those who are interested: [Changelog podcast](https://changelog.com/podcast/532) ### 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](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions ### Docs - [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks)