base on Official inference framework for 1-bit LLMs # bitnet.cpp [](https://opensource.org/licenses/MIT)  bitnet.cpp is the official inference framework for 1-bit LLMs (e.g., BitNet b1.58). It offers a suite of optimized kernels, that support **fast** and **lossless** inference of 1.58-bit models on CPU (with NPU and GPU support coming next). The first release of bitnet.cpp is to support inference on CPUs. bitnet.cpp achieves speedups of **1.37x** to **5.07x** on ARM CPUs, with larger models experiencing greater performance gains. Additionally, it reduces energy consumption by **55.4%** to **70.0%**, further boosting overall efficiency. On x86 CPUs, speedups range from **2.37x** to **6.17x** with energy reductions between **71.9%** to **82.2%**. Furthermore, bitnet.cpp can run a 100B BitNet b1.58 model on a single CPU, achieving speeds comparable to human reading (5-7 tokens per second), significantly enhancing the potential for running LLMs on local devices. Please refer to the [technical report](https://arxiv.org/abs/2410.16144) for more details. <img src="./assets/m2_performance.jpg" alt="m2_performance" width="800"/> <img src="./assets/intel_performance.jpg" alt="m2_performance" width="800"/> >The tested models are dummy setups used in a research context to demonstrate the inference performance of bitnet.cpp. ## Demo A demo of bitnet.cpp running a BitNet b1.58 3B model on Apple M2: https://github.com/user-attachments/assets/7f46b736-edec-4828-b809-4be780a3e5b1 ## What's New: - 11/08/2024 [BitNet a4.8: 4-bit Activations for 1-bit LLMs](https://arxiv.org/abs/2411.04965)  - 10/21/2024 [1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs](https://arxiv.org/abs/2410.16144) - 10/17/2024 bitnet.cpp 1.0 released. - 03/21/2024 [The-Era-of-1-bit-LLMs__Training_Tips_Code_FAQ](https://github.com/microsoft/unilm/blob/master/bitnet/The-Era-of-1-bit-LLMs__Training_Tips_Code_FAQ.pdf) - 02/27/2024 [The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits](https://arxiv.org/abs/2402.17764) - 10/17/2023 [BitNet: Scaling 1-bit Transformers for Large Language Models](https://arxiv.org/abs/2310.11453) ## Acknowledgements This project is based on the [llama.cpp](https://github.com/ggerganov/llama.cpp) framework. We would like to thank all the authors for their contributions to the open-source community. Also, bitnet.cpp's kernels are built on top of the Lookup Table methodologies pioneered in [T-MAC](https://github.com/microsoft/T-MAC/). For inference of general low-bit LLMs beyond ternary models, we recommend using T-MAC. ## Supported Models ❗️**We use existing 1-bit LLMs available on [Hugging Face](https://huggingface.co/) to demonstrate the inference capabilities of bitnet.cpp. These models are neither trained nor released by Microsoft. We hope the release of bitnet.cpp will inspire the development of 1-bit LLMs in large-scale settings in terms of model size and training tokens.** <table> </tr> <tr> <th rowspan="2">Model</th> <th rowspan="2">Parameters</th> <th rowspan="2">CPU</th> <th colspan="3">Kernel</th> </tr> <tr> <th>I2_S</th> <th>TL1</th> <th>TL2</th> </tr> <tr> <td rowspan="2"><a href="https://huggingface.co/1bitLLM/bitnet_b1_58-large">bitnet_b1_58-large</a></td> <td rowspan="2">0.7B</td> <td>x86</td> <td>&#9989;</td> <td>&#10060;</td> <td>&#9989;</td> </tr> <tr> <td>ARM</td> <td>&#9989;</td> <td>&#9989;</td> <td>&#10060;</td> </tr> <tr> <td rowspan="2"><a href="https://huggingface.co/1bitLLM/bitnet_b1_58-3B">bitnet_b1_58-3B</a></td> <td rowspan="2">3.3B</td> <td>x86</td> <td>&#10060;</td> <td>&#10060;</td> <td>&#9989;</td> </tr> <tr> <td>ARM</td> <td>&#10060;</td> <td>&#9989;</td> <td>&#10060;</td> </tr> <tr> <td rowspan="2"><a href="https://huggingface.co/HF1BitLLM/Llama3-8B-1.58-100B-tokens">Llama3-8B-1.58-100B-tokens</a></td> <td rowspan="2">8.0B</td> <td>x86</td> <td>&#9989;</td> <td>&#10060;</td> <td>&#9989;</td> </tr> <tr> <td>ARM</td> <td>&#9989;</td> <td>&#9989;</td> <td>&#10060;</td> </tr> <tr> <td rowspan="2"><a href="https://huggingface.co/collections/tiiuae/falcon3-67605ae03578be86e4e87026">Falcon3 Family</a></td> <td rowspan="2">1B-10B</td> <td>x86</td> <td>&#9989;</td> <td>&#10060;</td> <td>&#9989;</td> </tr> <tr> <td>ARM</td> <td>&#9989;</td> <td>&#9989;</td> <td>&#10060;</td> </tr> </table> ## Installation ### Requirements - python>=3.9 - cmake>=3.22 - clang>=18 - For Windows users, install [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/). In the installer, toggle on at least the following options(this also automatically installs the required additional tools like CMake): - Desktop-development with C++ - C++-CMake Tools for Windows - Git for Windows - C++-Clang Compiler for Windows - MS-Build Support for LLVM-Toolset (clang) - For Debian/Ubuntu users, you can download with [Automatic installation script](https://apt.llvm.org/) `bash -c "$(wget -O - https://apt.llvm.org/llvm.sh)"` - conda (highly recommend) ### Build from source > [!IMPORTANT] > If you are using Windows, please remember to always use a Developer Command Prompt / PowerShell for VS2022 for the following commands 1. Clone the repo ```bash git clone --recursive https://github.com/microsoft/BitNet.git cd BitNet ``` 2. Install the dependencies ```bash # (Recommended) Create a new conda environment conda create -n bitnet-cpp python=3.9 conda activate bitnet-cpp pip install -r requirements.txt ``` 3. Build the project ```bash # Download the model from Hugging Face, convert it to quantized gguf format, and build the project python setup_env.py --hf-repo tiiuae/Falcon3-7B-Instruct-1.58bit -q i2_s # Or you can manually download the model and run with local path huggingface-cli download tiiuae/Falcon3-7B-Instruct-1.58bit --local-dir models/Falcon3-7B-Instruct-1.58bit python setup_env.py -md models/Falcon3-7B-Instruct-1.58bit -q i2_s ``` <pre> usage: setup_env.py [-h] [--hf-repo {1bitLLM/bitnet_b1_58-large,1bitLLM/bitnet_b1_58-3B,HF1BitLLM/Llama3-8B-1.58-100B-tokens,tiiuae/Falcon3-1B-Instruct-1.58bit,tiiuae/Falcon3-3B-Instruct-1.58bit,tiiuae/Falcon3-7B-Instruct-1.58bit,tiiuae/Falcon3-10B-Instruct-1.58bit}] [--model-dir MODEL_DIR] [--log-dir LOG_DIR] [--quant-type {i2_s,tl1}] [--quant-embd] [--use-pretuned] Setup the environment for running inference optional arguments: -h, --help show this help message and exit --hf-repo {1bitLLM/bitnet_b1_58-large,1bitLLM/bitnet_b1_58-3B,HF1BitLLM/Llama3-8B-1.58-100B-tokens,tiiuae/Falcon3-1B-Instruct-1.58bit,tiiuae/Falcon3-3B-Instruct-1.58bit,tiiuae/Falcon3-7B-Instruct-1.58bit,tiiuae/Falcon3-10B-Instruct-1.58bit}, -hr {1bitLLM/bitnet_b1_58-large,1bitLLM/bitnet_b1_58-3B,HF1BitLLM/Llama3-8B-1.58-100B-tokens,tiiuae/Falcon3-1B-Instruct-1.58bit,tiiuae/Falcon3-3B-Instruct-1.58bit,tiiuae/Falcon3-7B-Instruct-1.58bit,tiiuae/Falcon3-10B-Instruct-1.58bit} Model used for inference --model-dir MODEL_DIR, -md MODEL_DIR Directory to save/load the model --log-dir LOG_DIR, -ld LOG_DIR Directory to save the logging info --quant-type {i2_s,tl1}, -q {i2_s,tl1} Quantization type --quant-embd Quantize the embeddings to f16 --use-pretuned, -p Use the pretuned kernel parameters </pre> ## Usage ### Basic usage ```bash # Run inference with the quantized model python run_inference.py -m models/Falcon3-7B-Instruct-1.58bit/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv ``` <pre> usage: run_inference.py [-h] [-m MODEL] [-n N_PREDICT] -p PROMPT [-t THREADS] [-c CTX_SIZE] [-temp TEMPERATURE] [-cnv] Run inference optional arguments: -h, --help show this help message and exit -m MODEL, --model MODEL Path to model file -n N_PREDICT, --n-predict N_PREDICT Number of tokens to predict when generating text -p PROMPT, --prompt PROMPT Prompt to generate text from -t THREADS, --threads THREADS Number of threads to use -c CTX_SIZE, --ctx-size CTX_SIZE Size of the prompt context -temp TEMPERATURE, --temperature TEMPERATURE Temperature, a hyperparameter that controls the randomness of the generated text -cnv, --conversation Whether to enable chat mode or not (for instruct models.) (When this option is turned on, the prompt specified by -p will be used as the system prompt.) </pre> ### Benchmark We provide scripts to run the inference benchmark providing a model. ``` usage: e2e_benchmark.py -m MODEL [-n N_TOKEN] [-p N_PROMPT] [-t THREADS] Setup the environment for running the inference required arguments: -m MODEL, --model MODEL Path to the model file. optional arguments: -h, --help Show this help message and exit. -n N_TOKEN, --n-token N_TOKEN Number of generated tokens. -p N_PROMPT, --n-prompt N_PROMPT Prompt to generate text from. -t THREADS, --threads THREADS Number of threads to use. ``` Here's a brief explanation of each argument: - `-m`, `--model`: The path to the model file. This is a required argument that must be provided when running the script. - `-n`, `--n-token`: The number of tokens to generate during the inference. It is an optional argument with a default value of 128. - `-p`, `--n-prompt`: The number of prompt tokens to use for generating text. This is an optional argument with a default value of 512. - `-t`, `--threads`: The number of threads to use for running the inference. It is an optional argument with a default value of 2. - `-h`, `--help`: Show the help message and exit. Use this argument to display usage information. For example: ```sh python utils/e2e_benchmark.py -m /path/to/model -n 200 -p 256 -t 4 ``` This command would run the inference benchmark using the model located at `/path/to/model`, generating 200 tokens from a 256 token prompt, utilizing 4 threads. For the model layout that do not supported by any public model, we provide scripts to generate a dummy model with the given model layout, and run the benchmark on your machine: ```bash python utils/generate-dummy-bitnet-model.py models/bitnet_b1_58-large --outfile models/dummy-bitnet-125m.tl1.gguf --outtype tl1 --model-size 125M # Run benchmark with the generated model, use -m to specify the model path, -p to specify the prompt processed, -n to specify the number of token to generate python utils/e2e_benchmark.py -m models/dummy-bitnet-125m.tl1.gguf -p 512 -n 128 ``` ", Assign "at most 3 tags" to the expected json: {"id":"12228","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"