![# LLaMA Factory](assets/logo.png) [![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Factory?style=social)](https://github.com/hiyouga/LLaMA-Factory/stargazers) [![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Factory)](LICENSE) [![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/commits/main) [![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/) [![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/) [![Citation](https://img.shields.io/badge/citation-44-green)](#projects-using-llama-factory) [![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls) [![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK) [![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai) [![Spaces](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue)](https://huggingface.co/spaces/hiyouga/LLaMA-Board) [![Studios](https://img.shields.io/badge/ModelScope-Open%20in%20Studios-blue)](https://modelscope.cn/studios/hiyouga/LLaMA-Board) [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing) [![GitHub Tread](https://trendshift.io/api/badge/repositories/4535)](https://trendshift.io/repositories/4535) 👋 Join our [WeChat](assets/wechat.jpg). \[ English | [中文](README_zh.md) \] **Fine-tuning a large language model can be easy as...** https://github.com/hiyouga/LLaMA-Factory/assets/16256802/9840a653-7e9c-41c8-ae89-7ace5698baf6 Choose your path: - **Colab**: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing - **Local machine**: Please refer to [usage](#getting-started) ## Table of Contents - [Features](#features) - [Benchmark](#benchmark) - [Changelog](#changelog) - [Supported Models](#supported-models) - [Supported Training Approaches](#supported-training-approaches) - [Provided Datasets](#provided-datasets) - [Requirement](#requirement) - [Getting Started](#getting-started) - [Projects using LLaMA Factory](#projects-using-llama-factory) - [License](#license) - [Citation](#citation) - [Acknowledgement](#acknowledgement) ## Features - **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc. - **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO and ORPO. - **Scalable resources**: 32-bit full-tuning, 16-bit freeze-tuning, 16-bit LoRA and 2/4/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8. - **Advanced algorithms**: GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and Agent tuning. - **Practical tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA. - **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, etc. - **Faster inference**: OpenAI-style API, Gradio UI and CLI with vLLM worker. ## Benchmark Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/ptuning), LLaMA Factory's LoRA tuning offers up to **3.7 times faster** training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory. ![benchmark](assets/benchmark.svg)
Definitions - **Training Speed**: the number of training samples processed per second during the training. (bs=4, cutoff_len=1024) - **Rouge Score**: Rouge-2 score on the development set of the [advertising text generation](https://aclanthology.org/D19-1321.pdf) task. (bs=4, cutoff_len=1024) - **GPU Memory**: Peak GPU memory usage in 4-bit quantized training. (bs=1, cutoff_len=1024) - We adopt `pre_seq_len=128` for ChatGLM's P-Tuning and `lora_rank=32` for LLaMA Factory's LoRA tuning.
## Changelog [24/04/26] We supported fine-tuning the **LLaVA-1.5** multimodal LLMs. See `examples/lora_single_gpu/sft_mllm.sh` for usage. [24/04/22] We provided a **[Colab notebook](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)** for fine-tuning the Llama-3 model on a free T4 GPU. Two Llama-3-derived models fine-tuned using LLaMA Factory are available at Hugging Face, check [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) and [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese) for details. [24/04/21] We supported **[Mixture-of-Depths](https://arxiv.org/abs/2404.02258)** according to [AstraMindAI's implementation](https://github.com/astramind-ai/Mixture-of-depths). See `examples/extras/mod` for usage. [24/04/16] We supported **[BAdam](https://arxiv.org/abs/2404.02827)**. See `examples/extras/badam` for usage. [24/04/16] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s long-sequence training (Llama-2-7B-56k within 24GB). It achieves **117%** speed and **50%** memory compared with FlashAttention-2, more benchmarks can be found in [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison).
Full Changelog [24/03/31] We supported **[ORPO](https://arxiv.org/abs/2403.07691)**. See `examples/lora_single_gpu` for usage. [24/03/21] Our paper "[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://arxiv.org/abs/2403.13372)" is available at arXiv! [24/03/20] We supported **FSDP+QLoRA** that fine-tunes a 70B model on 2x24GB GPUs. See `examples/extras/fsdp_qlora` for usage. [24/03/13] We supported **[LoRA+](https://arxiv.org/abs/2402.12354)**. See `examples/extras/loraplus` for usage. [24/03/07] We supported gradient low-rank projection (**[GaLore](https://arxiv.org/abs/2403.03507)**) algorithm. See `examples/extras/galore` for usage. [24/03/07] We integrated **[vLLM](https://github.com/vllm-project/vllm)** for faster and concurrent inference. Try `--infer_backend vllm` to enjoy **270%** inference speed. (LoRA is not yet supported, merge it first.) [24/02/28] We supported weight-decomposed LoRA (**[DoRA](https://arxiv.org/abs/2402.09353)**). Try `--use_dora` to activate DoRA training. [24/02/15] We supported **block expansion** proposed by [LLaMA Pro](https://github.com/TencentARC/LLaMA-Pro). See `examples/extras/llama_pro` for usage. [24/02/05] Qwen1.5 (Qwen2 beta version) series models are supported in LLaMA-Factory. Check this [blog post](https://qwenlm.github.io/blog/qwen1.5/) for details. [24/01/18] We supported **agent tuning** for most models, equipping model with tool using abilities by fine-tuning with `--dataset glaive_toolcall`. [23/12/23] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s implementation to boost LoRA tuning for the LLaMA, Mistral and Yi models. Try `--use_unsloth` argument to activate unsloth patch. It achieves **170%** speed in our benchmark, check [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison) for details. [23/12/12] We supported fine-tuning the latest MoE model **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)** in our framework. See hardware requirement [here](#hardware-requirement). [23/12/01] We supported downloading pre-trained models and datasets from the **[ModelScope Hub](https://modelscope.cn/models)** for Chinese mainland users. See [this tutorial](#use-modelscope-hub-optional) for usage. [23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `--neftune_noise_alpha` argument to activate NEFTune, e.g., `--neftune_noise_alpha 5`. [23/09/27] We supported **$S^2$-Attn** proposed by [LongLoRA](https://github.com/dvlab-research/LongLoRA) for the LLaMA models. Try `--shift_attn` argument to enable shift short attention. [23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [this example](#evaluation) to evaluate your models. [23/09/10] We supported **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**. Try `--flash_attn fa2` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs. [23/08/12] We supported **RoPE scaling** to extend the context length of the LLaMA models. Try `--rope_scaling linear` argument in training and `--rope_scaling dynamic` argument at inference to extrapolate the position embeddings. [23/08/11] We supported **[DPO training](https://arxiv.org/abs/2305.18290)** for instruction-tuned models. See [this example](#dpo-training) to train your models. [23/07/31] We supported **dataset streaming**. Try `--streaming` and `--max_steps 10000` arguments to load your dataset in streaming mode. [23/07/29] We released two instruction-tuned 13B models at Hugging Face. See these Hugging Face Repos ([LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/Baichuan-13B-sft)) for details. [23/07/18] We developed an **all-in-one Web UI** for training, evaluation and inference. Try `train_web.py` to fine-tune models in your Web browser. Thank [@KanadeSiina](https://github.com/KanadeSiina) and [@codemayq](https://github.com/codemayq) for their efforts in the development. [23/07/09] We released **[FastEdit](https://github.com/hiyouga/FastEdit)** ⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow [FastEdit](https://github.com/hiyouga/FastEdit) if you are interested. [23/06/29] We provided a **reproducible example** of training a chat model using instruction-following datasets, see [Baichuan-7B-sft](https://huggingface.co/hiyouga/Baichuan-7B-sft) for details. [23/06/22] We aligned the [demo API](src/api_demo.py) with the [OpenAI's](https://platform.openai.com/docs/api-reference/chat) format where you can insert the fine-tuned model in **arbitrary ChatGPT-based applications**. [23/06/03] We supported quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). Try `--quantization_bit 4/8` argument to work with quantized models.
## Supported Models | Model | Model size | Default module | Template | | -------------------------------------------------------- | -------------------------------- | ----------------- | --------- | | [Baichuan2](https://huggingface.co/baichuan-inc) | 7B/13B | W_pack | baichuan2 | | [BLOOM](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - | | [BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - | | [ChatGLM3](https://huggingface.co/THUDM) | 6B | query_key_value | chatglm3 | | [Command-R](https://huggingface.co/CohereForAI) | 35B/104B | q_proj,v_proj | cohere | | [DeepSeek (MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | q_proj,v_proj | deepseek | | [Falcon](https://huggingface.co/tiiuae) | 7B/40B/180B | query_key_value | falcon | | [Gemma/CodeGemma](https://huggingface.co/google) | 2B/7B | q_proj,v_proj | gemma | | [InternLM2](https://huggingface.co/internlm) | 7B/20B | wqkv | intern2 | | [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | q_proj,v_proj | - | | [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 | | [LLaMA-3](https://huggingface.co/meta-llama) | 8B/70B | q_proj,v_proj | llama3 | | [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | q_proj,v_proj | vicuna | | [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | q_proj,v_proj | mistral | | [OLMo](https://huggingface.co/allenai) | 1B/7B | q_proj,v_proj | - | | [Phi-1.5/2](https://huggingface.co/microsoft) | 1.3B/2.7B | q_proj,v_proj | - | | [Phi-3](https://huggingface.co/microsoft) | 3.8B | qkv_proj | phi | | [Qwen](https://huggingface.co/Qwen) | 1.8B/7B/14B/72B | c_attn | qwen | | [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen) | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | q_proj,v_proj | qwen | | [StarCoder2](https://huggingface.co/bigcode) | 3B/7B/15B | q_proj,v_proj | - | | [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | q_proj,v_proj | xverse | | [Yi](https://huggingface.co/01-ai) | 6B/9B/34B | q_proj,v_proj | yi | | [Yuan](https://huggingface.co/IEITYuan) | 2B/51B/102B | q_proj,v_proj | yuan | > [!NOTE] > **Default module** is used for the `--lora_target` argument, you can use `--lora_target all` to specify all the available modules for better convergence. > > For the "base" models, the `--template` argument can be chosen from `default`, `alpaca`, `vicuna` etc. But make sure to use the **corresponding template** for the "instruct/chat" models. > > Remember to use the **SAME** template in training and inference. Please refer to [constants.py](src/llmtuner/extras/constants.py) for a full list of models we supported. You also can add a custom chat template to [template.py](src/llmtuner/data/template.py). ## Supported Training Approaches | Approach | Full-tuning | Freeze-tuning | LoRA | QLoRA | | ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ | | Pre-Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | | Supervised Fine-Tuning | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | | Reward Modeling | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | | PPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | | DPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | | ORPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | ## Provided Datasets
Pre-training datasets - [Wiki Demo (en)](data/wiki_demo.txt) - [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) - [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2) - [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220) - [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered) - [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile) - [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B) - [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack) - [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
Supervised fine-tuning datasets - [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca) - [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca) - [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) - [Identity (en&zh)](data/identity.json) - [Open Assistant (zh)](https://huggingface.co/datasets/OpenAssistant/oasst1) - [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection) - [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) - [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN) - [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN) - [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN) - [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M) - [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M) - [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M) - [UltraChat (en)](https://github.com/thunlp/UltraChat) - [LIMA (en)](https://huggingface.co/datasets/GAIR/lima) - [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) - [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) - [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT) - [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca) - [SlimOrca (en)](https://huggingface.co/datasets/Open-Orca/SlimOrca) - [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) - [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M) - [Wiki QA (en)](https://huggingface.co/datasets/wiki_qa) - [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa) - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn) - [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar) - [deepctrl (en&zh)](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data) - [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen) - [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k) - [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4) - [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) - [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct) - [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) - [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k) - [Glaive Function Calling V2 (en)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) - [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) - [LLaVA mixed (en&zh)](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k) - [Open Assistant (de)](https://huggingface.co/datasets/mayflowergmbh/oasst_de) - [Dolly 15k (de)](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de) - [Alpaca GPT4 (de)](https://huggingface.co/datasets/mayflowergmbh/alpaca-gpt4_de) - [OpenSchnabeltier (de)](https://huggingface.co/datasets/mayflowergmbh/openschnabeltier_de) - [Evol Instruct (de)](https://huggingface.co/datasets/mayflowergmbh/evol-instruct_de) - [Dolphin (de)](https://huggingface.co/datasets/mayflowergmbh/dolphin_de) - [Booksum (de)](https://huggingface.co/datasets/mayflowergmbh/booksum_de) - [Airoboros (de)](https://huggingface.co/datasets/mayflowergmbh/airoboros-3.0_de) - [Ultrachat (de)](https://huggingface.co/datasets/mayflowergmbh/ultra-chat_de)
Preference datasets - [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf) - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) - [Orca DPO (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs) - [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar) - [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k) - [Open Assistant (zh)](https://huggingface.co/datasets/OpenAssistant/oasst1) - [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)
Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands. ```bash pip install --upgrade huggingface_hub huggingface-cli login ``` ## Requirement | Mandatory | Minimum | Recommend | | ------------ | ------- | --------- | | python | 3.8 | 3.10 | | torch | 1.13.1 | 2.2.0 | | transformers | 4.37.2 | 4.40.1 | | datasets | 2.14.3 | 2.19.1 | | accelerate | 0.27.2 | 0.30.0 | | peft | 0.9.0 | 0.10.0 | | trl | 0.8.1 | 0.8.6 | | Optional | Minimum | Recommend | | ------------ | ------- | --------- | | CUDA | 11.6 | 12.2 | | deepspeed | 0.10.0 | 0.14.0 | | bitsandbytes | 0.39.0 | 0.43.1 | | vllm | 0.4.0 | 0.4.2 | | flash-attn | 2.3.0 | 2.5.8 | ### Hardware Requirement \* *estimated* | Method | Bits | 7B | 13B | 30B | 70B | 110B | 8x7B | 8x22B | | ----------------- | ---- | ----- | ----- | ----- | ------ | ------ | ----- | ------ | | Full | AMP | 120GB | 240GB | 600GB | 1200GB | 2000GB | 900GB | 2400GB | | Full | 16 | 60GB | 120GB | 300GB | 600GB | 900GB | 400GB | 1200GB | | Freeze | 16 | 20GB | 40GB | 80GB | 200GB | 360GB | 160GB | 400GB | | LoRA/GaLore/BAdam | 16 | 16GB | 32GB | 64GB | 160GB | 240GB | 120GB | 320GB | | QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | 140GB | 60GB | 160GB | | QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 72GB | 30GB | 96GB | | QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | 48GB | 18GB | 48GB | ## Getting Started ### Installation > [!IMPORTANT] > Installation is mandatory. ```bash git clone https://github.com/hiyouga/LLaMA-Factory.git cd LLaMA-Factory pip install -e .[torch,metrics] ``` Extra dependencies available: torch, metrics, deepspeed, bitsandbytes, vllm, galore, badam, gptq, awq, aqlm, qwen, modelscope, quality > [!TIP] > Use `pip install --no-deps -e .` to resolve package conflicts.
For Windows users If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you will be required to install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.2, please select the appropriate [release version](https://github.com/jllllll/bitsandbytes-windows-webui/releases/tag/wheels) based on your CUDA version. ```bash pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl ``` To enable FlashAttention-2 on the Windows platform, you need to install the precompiled `flash-attn` library, which supports CUDA 12.1 to 12.2. Please download the corresponding version from [flash-attention](https://github.com/bdashore3/flash-attention/releases) based on your requirements.
### Data Preparation Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use datasets on HuggingFace / ModelScope hub or load the dataset in local disk. > [!NOTE] > Please update `data/dataset_info.json` to use your custom dataset. ### Quickstart Use the following 3 commands to run LoRA **fine-tuning**, **inference** and **merging** of the Llama3-8B-Instruct model, respectively. ```bash CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml ``` See [examples/README.md](examples/README.md) for advanced usage (including distributed training). > [!TIP] > Use `llamafactory-cli help` to show help information. ### Fine-Tuning with LLaMA Board GUI (powered by [Gradio](https://github.com/gradio-app/gradio)) > [!IMPORTANT] > LLaMA Board GUI only supports training on a single GPU. #### Use local environment ```bash CUDA_VISIBLE_DEVICES=0 GRADIO_SHARE=1 llamafactory-cli webui ```
For Alibaba Cloud PAI or AutoDL users If you encountered display problems in LLaMA Board on Alibaba Cloud PAI, try using the following command to set environment variables before starting LLaMA Board: ```bash export GRADIO_SERVER_PORT=7860 GRADIO_ROOT_PATH=/${JUPYTER_NAME}/proxy/7860/ ``` If you are using AutoDL, please install a specific version of Gradio: ```bash pip install gradio==4.10.0 ```
#### Use Docker ```bash docker build -f ./Dockerfile -t llama-factory:latest . docker run --gpus=all \ -v ./hf_cache:/root/.cache/huggingface/ \ -v ./data:/app/data \ -v ./output:/app/output \ -e CUDA_VISIBLE_DEVICES=0 \ -p 7860:7860 \ --shm-size 16G \ --name llama_factory \ -d llama-factory:latest ``` #### Use Docker Compose ```bash docker compose -f ./docker-compose.yml up -d ```
Details about volume - hf_cache: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory. - data: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI. - output: Set export dir to this location so that the merged result can be accessed directly on the host machine.
### Deploy with OpenAI-style API and vLLM ```bash CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml ``` ### Download from ModelScope Hub If you have trouble with downloading models and datasets from Hugging Face, you can use ModelScope. ```bash export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows ``` Train the model by specifying a model ID of the ModelScope Hub as the `--model_name_or_path`. You can find a full list of model IDs at [ModelScope Hub](https://modelscope.cn/models), e.g., `LLM-Research/Meta-Llama-3-8B-Instruct`. ## Projects using LLaMA Factory If you have a project that should be incorporated, please contact via email or create a pull request.
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[[arxiv]](https://arxiv.org/abs/2404.18585) 1. **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B. 1. **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge. 1. **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B. 1. **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B. 1. **[MachineMindset](https://github.com/PKU-YuanGroup/Machine-Mindset/)**: A series of MBTI Personality large language models, capable of giving any LLM 16 different personality types based on different datasets and training methods. 1. **[Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3)**: A large language model specialized in generate metadata for stable diffusion. [[🤗Demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt) 1. **[Chinese-LLaVA-Med](https://github.com/BUAADreamer/Chinese-LLaVA-Med)**: A multimodal large language model specialized in Chinese medical domain, based on LLaVA-1.5-7B.
## License This repository is licensed under the [Apache-2.0 License](LICENSE). Please follow the model licenses to use the corresponding model weights: [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command-R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2/LLaVA-1.5](https://ai.meta.com/llama/license/) / [LLaMA-3](https://llama.meta.com/llama3/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan) ## Citation If this work is helpful, please kindly cite as: ```bibtex @article{zheng2024llamafactory, title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models}, author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Yongqiang Ma}, journal={arXiv preprint arXiv:2403.13372}, year={2024}, url={http://arxiv.org/abs/2403.13372} } ``` ## Acknowledgement This repo benefits from [PEFT](https://github.com/huggingface/peft), [TRL](https://github.com/huggingface/trl), [QLoRA](https://github.com/artidoro/qlora) and [FastChat](https://github.com/lm-sys/FastChat). Thanks for their wonderful works. ## Star History ![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Factory&type=Date)