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[](https://github.com/hiyouga/LLaMA-Factory/stargazers)
[](LICENSE)
[](https://github.com/hiyouga/LLaMA-Factory/commits/main)
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[](https://pypi.org/project/llmtuner/)
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[](https://pypi.org/project/llmtuner/)
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[](#projects-using-llama-factory)
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[](https://github.com/hiyouga/LLaMA-Factory/pulls)
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[](https://discord.gg/rKfvV9r9FK)
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[](https://twitter.com/llamafactory_ai)
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[](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
[](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
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[](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)
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👋 Join our [WeChat ](assets/wechat.jpg ).
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\[ English | [中文 ](README_zh.md ) \]
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**Fine-tuning a large language model can be easy as...**
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https://github.com/hiyouga/LLaMA-Factory/assets/16256802/9840a653-7e9c-41c8-ae89-7ace5698baf6
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Choose your path:
- **Colab**: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
- **Local machine**: Please refer to [usage ](#getting-started )
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## Table of Contents
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- [Features ](#features )
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- [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 )
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## Features
- **Various models**: LLaMA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
- **Integrated methods**: (Continuous) pre-training, supervised fine-tuning, reward modeling, PPO and DPO.
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- **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.
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- **Advanced algorithms**: GaLore, DoRA, LongLoRA, LLaMA Pro, LoRA+, LoftQ and Agent tuning.
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- **Practical tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA.
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- **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, etc.
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- **Faster inference**: OpenAI-style API, Gradio UI and CLI with vLLM worker.
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## Benchmark
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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.
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< details > < summary > Definitions< / summary >
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- **Training Speed**: the number of training samples processed per second during the training. (bs=4, cutoff_len=1024)
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- **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)
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- **GPU Memory**: Peak GPU memory usage in 4-bit quantized training. (bs=1, cutoff_len=1024)
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- We adopt `pre_seq_len=128` for ChatGLM's P-Tuning and `lora_rank=32` for LLaMA-Factory's LoRA tuning.
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< / details >
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## Changelog
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[24/03/21] Our paper "[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://arxiv.org/abs/2403.13372)" is available at arXiv!
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[24/03/20] We supported **FSDP+QLoRA** that fine-tunes a 70B model on 2x24GB GPUs. See `examples/fsdp_qlora` for usage.
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[24/03/13] We supported ** [LoRA+ ](https://arxiv.org/abs/2402.12354 )**. See `examples/extras/loraplus` for usage.
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[24/03/07] We supported gradient low-rank projection (**[GaLore](https://arxiv.org/abs/2403.03507)**) algorithm. See `examples/extras/galore` for usage.
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[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.)
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< details > < summary > Full Changelog< / summary >
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[24/02/28] We supported weight-decomposed LoRA (**[DoRA](https://arxiv.org/abs/2402.09353)**). Try `--use_dora` to activate DoRA training.
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[24/02/15] We supported **block expansion** proposed by [LLaMA Pro ](https://github.com/TencentARC/LLaMA-Pro ). See `examples/extras/llama_pro` for usage.
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[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.
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[24/01/18] We supported **agent tuning** for most models, equipping model with tool using abilities by fine-tuning with `--dataset glaive_toolcall` .
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[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.
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[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 ).
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[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.
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[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` .
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[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.
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[23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [this example ](#evaluation ) to evaluate your models.
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[23/09/10] We supported ** [FlashAttention-2 ](https://github.com/Dao-AILab/flash-attention )**. Try `--flash_attn` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs.
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[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.
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[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.
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[23/07/31] We supported **dataset streaming** . Try `--streaming` and `--max_steps 10000` arguments to load your dataset in streaming mode.
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[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.
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[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.
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[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.
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[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.
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[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.
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< / details >
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## Supported Models
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| Model | Model size | Default module | Template |
| -------------------------------------------------------- | --------------------------- | ----------------- | --------- |
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| [Baichuan2 ](https://huggingface.co/baichuan-inc ) | 7B/13B | W_pack | baichuan2 |
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| [BLOOM ](https://huggingface.co/bigscience/bloom ) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
| [BLOOMZ ](https://huggingface.co/bigscience/bloomz ) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
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| [ChatGLM3 ](https://huggingface.co/THUDM/chatglm3-6b ) | 6B | query_key_value | chatglm3 |
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| [DeepSeek (MoE) ](https://huggingface.co/deepseek-ai ) | 7B/16B/67B | q_proj,v_proj | deepseek |
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| [Falcon ](https://huggingface.co/tiiuae ) | 7B/40B/180B | query_key_value | falcon |
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| [Gemma ](https://huggingface.co/google ) | 2B/7B | q_proj,v_proj | gemma |
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| [InternLM2 ](https://huggingface.co/internlm ) | 7B/20B | wqkv | intern2 |
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| [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 |
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| [Mistral ](https://huggingface.co/mistralai ) | 7B | q_proj,v_proj | mistral |
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| [Mixtral ](https://huggingface.co/mistralai ) | 8x7B | q_proj,v_proj | mistral |
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| [OLMo ](https://huggingface.co/allenai ) | 1B/7B | att_proj | olmo |
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| [Phi-1.5/2 ](https://huggingface.co/microsoft ) | 1.3B/2.7B | q_proj,v_proj | - |
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| [Qwen ](https://huggingface.co/Qwen ) | 1.8B/7B/14B/72B | c_attn | qwen |
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| [Qwen1.5 ](https://huggingface.co/Qwen ) | 0.5B/1.8B/4B/7B/14B/72B | q_proj,v_proj | qwen |
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| [StarCoder2 ](https://huggingface.co/bigcode ) | 3B/7B/15B | q_proj,v_proj | - |
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| [XVERSE ](https://huggingface.co/xverse ) | 7B/13B/65B | q_proj,v_proj | xverse |
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| [Yi ](https://huggingface.co/01-ai ) | 6B/9B/34B | q_proj,v_proj | yi |
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| [Yuan ](https://huggingface.co/IEITYuan ) | 2B/51B/102B | q_proj,v_proj | yuan |
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> [!NOTE]
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> **Default module** is used for the `--lora_target` argument, you can use `--lora_target all` to specify all the available modules.
>
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> 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 "chat" models.
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Please refer to [constants.py ](src/llmtuner/extras/constants.py ) for a full list of models we supported.
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You also can add a custom chat template to [template.py ](src/llmtuner/data/template.py ).
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## Supported Training Approaches
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| Approach | Full-tuning | Freeze-tuning | LoRA | QLoRA |
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| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
| 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: |
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| 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: |
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> [!NOTE]
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> Use `--quantization_bit 4` argument to enable QLoRA.
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## Provided Datasets
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< details > < summary > Pre-training datasets< / summary >
- [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 )
< / details >
< details > < summary > Supervised fine-tuning datasets< / summary >
- [Stanford Alpaca (en) ](https://github.com/tatsu-lab/stanford_alpaca )
- [Stanford Alpaca (zh) ](https://github.com/ymcui/Chinese-LLaMA-Alpaca )
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- [Alpaca GPT4 (en&zh) ](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM )
- [Self Cognition (zh) ](data/self_cognition.json )
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- [Open Assistant (multilingual) ](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 )
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- [OpenOrca (en) ](https://huggingface.co/datasets/Open-Orca/OpenOrca )
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- [SlimOrca (en) ](https://huggingface.co/datasets/Open-Orca/SlimOrca )
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- [MathInstruct (en) ](https://huggingface.co/datasets/TIGER-Lab/MathInstruct )
- [Firefly 1.1M (zh) ](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M )
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- [Wiki QA (en) ](https://huggingface.co/datasets/wiki_qa )
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- [Web QA (zh) ](https://huggingface.co/datasets/suolyer/webqa )
- [WebNovel (zh) ](https://huggingface.co/datasets/zxbsmk/webnovel_cn )
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- [Nectar (en) ](https://huggingface.co/datasets/berkeley-nest/Nectar )
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- [deepctrl (en&zh) ](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data )
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- [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 )
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- [LMSYS Chat 1M (en) ](https://huggingface.co/datasets/lmsys/lmsys-chat-1m )
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- [Evol Instruct V2 (en) ](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k )
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- [Glaive Function Calling V2 (en) ](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2 )
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- [Cosmopedia (en) ](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia )
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- [Open Assistant (de) ](https://huggingface.co/datasets/mayflowergmbh/oasst_de )
- [Dolly 15k (de) ](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de )
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- [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 )
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< / details >
< details > < summary > Preference datasets< / summary >
- [HH-RLHF (en) ](https://huggingface.co/datasets/Anthropic/hh-rlhf )
- [Open Assistant (multilingual) ](https://huggingface.co/datasets/OpenAssistant/oasst1 )
- [GPT-4 Generated Data (en&zh) ](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM )
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- [Orca DPO (en) ](https://huggingface.co/datasets/Intel/orca_dpo_pairs )
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- [Nectar (en) ](https://huggingface.co/datasets/berkeley-nest/Nectar )
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- [Orca DPO (de) ](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de )
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< / details >
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Please refer to [data/README.md ](data/README.md ) for details.
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Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands.
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```bash
pip install --upgrade huggingface_hub
huggingface-cli login
```
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## Requirement
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| Mandatory | Minimum | Recommend |
| ------------ | ------- | --------- |
| python | 3.8 | 3.10 |
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| torch | 1.13.1 | 2.2.0 |
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| transformers | 4.37.2 | 4.39.1 |
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| datasets | 2.14.3 | 2.17.1 |
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| accelerate | 0.27.2 | 0.28.0 |
| peft | 0.9.0 | 0.10.0 |
| trl | 0.8.1 | 0.8.1 |
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| Optional | Minimum | Recommend |
| ------------ | ------- | --------- |
| CUDA | 11.6 | 12.2 |
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| deepspeed | 0.10.0 | 0.14.0 |
| bitsandbytes | 0.39.0 | 0.43.0 |
| flash-attn | 2.3.0 | 2.5.6 |
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### Hardware Requirement
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\* *estimated*
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| Method | Bits | 7B | 13B | 30B | 70B | 8x7B |
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| ------ | ---- | ----- | ----- | ----- | ------ | ------ |
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| Full | AMP | 120GB | 240GB | 600GB | 1200GB | 900GB |
| Full | 16 | 60GB | 120GB | 300GB | 600GB | 400GB |
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| GaLore | 16 | 16GB | 32GB | 64GB | 160GB | 120GB |
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| Freeze | 16 | 20GB | 40GB | 80GB | 200GB | 160GB |
| LoRA | 16 | 16GB | 32GB | 64GB | 160GB | 120GB |
| QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | 60GB |
| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 30GB |
| QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | 18GB |
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## Getting Started
### Data Preparation (optional)
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Please refer to [data/README.md ](data/README.md ) for checking the details about the format of dataset files. You can either use a single `.json` file or a [dataset loading script ](https://huggingface.co/docs/datasets/dataset_script ) with multiple files to create a custom dataset.
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> [!NOTE]
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> Please update `data/dataset_info.json` to use your custom dataset. About the format of this file, please refer to `data/README.md`.
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### Dependence Installation (optional)
```bash
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git clone https://github.com/hiyouga/LLaMA-Factory.git
conda create -n llama_factory python=3.10
conda activate llama_factory
cd LLaMA-Factory
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pip install -r requirements.txt
```
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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.
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```bash
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pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl
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```
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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.
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### Use ModelScope Hub (optional)
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If you have trouble with downloading models and datasets from Hugging Face, you can use LLaMA-Factory together with ModelScope in the following manner.
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```bash
export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows
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```
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Then you can train the corresponding model by specifying a model ID of the ModelScope Hub. (find a full list of model IDs at [ModelScope Hub ](https://modelscope.cn/models ))
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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--model_name_or_path modelscope/Llama-2-7b-ms \
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... # arguments (same as below)
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```
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LLaMA Board also supports using the models and datasets on the ModelScope Hub.
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```bash
CUDA_VISIBLE_DEVICES=0 USE_MODELSCOPE_HUB=1 python src/train_web.py
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```
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### Train on a single GPU
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> [!IMPORTANT]
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> If you want to train models on multiple GPUs, please refer to [Distributed Training](#distributed-training).
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#### LLaMA Board GUI
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_web.py
```
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#### Pre-Training
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage pt \
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--do_train \
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--model_name_or_path path_to_llama_model \
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--dataset wiki_demo \
--finetuning_type lora \
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--lora_target q_proj,v_proj \
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--output_dir path_to_pt_checkpoint \
--overwrite_cache \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--plot_loss \
--fp16
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```
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#### Supervised Fine-Tuning
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage sft \
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--do_train \
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--model_name_or_path path_to_llama_model \
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--dataset alpaca_gpt4_en \
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--template default \
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--finetuning_type lora \
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--lora_target q_proj,v_proj \
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--output_dir path_to_sft_checkpoint \
--overwrite_cache \
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--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--plot_loss \
--fp16
```
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#### Reward Modeling
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage rm \
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--do_train \
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--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_sft_checkpoint \
--create_new_adapter \
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--dataset comparison_gpt4_en \
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--template default \
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--finetuning_type lora \
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--lora_target q_proj,v_proj \
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--output_dir path_to_rm_checkpoint \
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--per_device_train_batch_size 2 \
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--gradient_accumulation_steps 4 \
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--lr_scheduler_type cosine \
--logging_steps 10 \
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--save_steps 1000 \
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--learning_rate 1e-5 \
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--num_train_epochs 1.0 \
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--plot_loss \
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--fp16
```
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#### PPO Training
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage ppo \
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--do_train \
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--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_sft_checkpoint \
--create_new_adapter \
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--dataset alpaca_gpt4_en \
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--template default \
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--finetuning_type lora \
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--lora_target q_proj,v_proj \
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--reward_model path_to_rm_checkpoint \
--output_dir path_to_ppo_checkpoint \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
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--top_k 0 \
--top_p 0.9 \
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--logging_steps 10 \
--save_steps 1000 \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
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--plot_loss \
--fp16
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```
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> [!TIP]
> Use `--adapter_name_or_path path_to_sft_checkpoint,path_to_ppo_checkpoint` to infer the fine-tuned model.
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> [!WARNING]
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> Use `--per_device_train_batch_size=1` for LLaMA-2 models in fp16 PPO training.
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#### DPO Training
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```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage dpo \
--do_train \
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--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_sft_checkpoint \
--create_new_adapter \
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--dataset comparison_gpt4_en \
--template default \
--finetuning_type lora \
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--lora_target q_proj,v_proj \
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--output_dir path_to_dpo_checkpoint \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
--plot_loss \
--fp16
```
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> [!TIP]
> Use `--adapter_name_or_path path_to_sft_checkpoint,path_to_dpo_checkpoint` to infer the fine-tuned model.
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### Distributed Training
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#### Use Huggingface Accelerate
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```bash
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accelerate launch --config_file config.yaml src/train_bash.py \
--ddp_timeout 180000000 \
... # arguments (same as above)
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```
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< details > < summary > Example config.yaml for LoRA training< / summary >
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```yaml
compute_environment: LOCAL_MACHINE
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debug: false
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distributed_type: MULTI_GPU
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downcast_bf16: 'no'
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gpu_ids: all
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machine_rank: 0
main_training_function: main
mixed_precision: fp16
num_machines: 1
num_processes: 4
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
```
< / details >
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> [!TIP]
> We commend using Accelerate for LoRA tuning.
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#### Use DeepSpeed
```bash
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deepspeed --num_gpus 8 src/train_bash.py \
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--deepspeed ds_config.json \
--ddp_timeout 180000000 \
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... # arguments (same as above)
```
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< details > < summary > Example ds_config.json for full-parameter training with DeepSpeed ZeRO-2< / summary >
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```json
{
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"train_batch_size": "auto",
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"train_micro_batch_size_per_gpu": "auto",
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"zero_allow_untested_optimizer": true,
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
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"initial_scale_power": 16,
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"hysteresis": 2,
"min_loss_scale": 1
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},
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"bf16": {
"enabled": "auto"
},
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"zero_optimization": {
"stage": 2,
"allgather_partitions": true,
"allgather_bucket_size": 5e8,
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"overlap_comm": true,
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"reduce_scatter": true,
"reduce_bucket_size": 5e8,
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"contiguous_gradients": true,
"round_robin_gradients": true
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}
}
```
< / details >
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> [!TIP]
> Refer to [examples](examples) for more training scripts.
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### Merge LoRA weights and export model
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/export_model.py \
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--model_name_or_path path_to_llama_model \
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--adapter_name_or_path path_to_checkpoint \
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--template default \
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--finetuning_type lora \
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--export_dir path_to_export \
--export_size 2 \
--export_legacy_format False
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```
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> [!WARNING]
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> Merging LoRA weights into a quantized model is not supported.
> [!TIP]
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> Use `--model_name_or_path path_to_export` solely to use the exported model.
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>
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> Use `--export_quantization_bit 4` and `--export_quantization_dataset data/c4_demo.json` to quantize the model with AutoGPTQ after merging the LoRA weights.
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### Inference with OpenAI-style API
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```bash
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CUDA_VISIBLE_DEVICES=0 API_PORT=8000 python src/api_demo.py \
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--model_name_or_path path_to_llama_model \
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--adapter_name_or_path path_to_checkpoint \
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--template default \
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--finetuning_type lora
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```
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> [!TIP]
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> Visit `http://localhost:8000/docs` for API documentation.
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### Inference with command line
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/cli_demo.py \
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--model_name_or_path path_to_llama_model \
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--adapter_name_or_path path_to_checkpoint \
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--template default \
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--finetuning_type lora
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```
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### Inference with web browser
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/web_demo.py \
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--model_name_or_path path_to_llama_model \
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--adapter_name_or_path path_to_checkpoint \
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--template default \
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--finetuning_type lora
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```
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### Evaluation
```bash
CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \
--model_name_or_path path_to_llama_model \
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--adapter_name_or_path path_to_checkpoint \
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--template vanilla \
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--finetuning_type lora \
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--task mmlu \
--split test \
--lang en \
--n_shot 5 \
--batch_size 4
```
### Predict
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage sft \
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--do_predict \
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--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_checkpoint \
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--dataset alpaca_gpt4_en \
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--template default \
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--finetuning_type lora \
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--output_dir path_to_predict_result \
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--per_device_eval_batch_size 1 \
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--max_samples 100 \
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--predict_with_generate \
--fp16
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```
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> [!WARNING]
> Use `--per_device_train_batch_size=1` for LLaMA-2 models in fp16 predict.
> [!TIP]
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> We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit predict.
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### Dockerize Training
#### Get ready
Necessary dockerized environment is needed, such as Docker or Docker Compose.
#### Docker support
```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 -p 7860:7860 --shm-size 16G --name llama_factory -d llama-factory:latest
```
#### Docker Compose support
```bash
docker compose -f ./docker-compose.yml up -d
```
> [!TIP]
> Details about volume:
> * hf_cache: Utilize Huggingface 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.
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## Projects using LLaMA Factory
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1. Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [[arxiv]](https://arxiv.org/abs/2308.02223)
1. Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [[arxiv]](https://arxiv.org/abs/2308.10092)
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1. Wang et al. UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language. 2023. [[arxiv]](https://arxiv.org/abs/2308.10526)
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1. Luceri et al. Leveraging Large Language Models to Detect Influence Campaigns in Social Media. 2023. [[arxiv]](https://arxiv.org/abs/2311.07816)
1. Zhang et al. Alleviating Hallucinations of Large Language Models through Induced Hallucinations. 2023. [[arxiv]](https://arxiv.org/abs/2312.15710)
2024-02-25 15:18:58 +08:00
1. Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2401.04319)
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1. Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2401.07286)
2024-02-25 15:18:58 +08:00
1. Choi et al. FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2402.05904)
1. Zhang et al. AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts. 2024. [[arxiv]](https://arxiv.org/abs/2402.07625)
1. Lyu et al. KnowTuning: Knowledge-aware Fine-tuning for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11176)
1. Yang et al. LaCo: Large Language Model Pruning via Layer Collaps. 2024. [[arxiv]](https://arxiv.org/abs/2402.11187)
1. Bhardwaj et al. Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic. 2024. [[arxiv]](https://arxiv.org/abs/2402.11746)
1. Yang et al. Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11801)
1. Yi et al. Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding. 2024. [[arxiv]](https://arxiv.org/abs/2402.11809)
1. Cao et al. Head-wise Shareable Attention for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11819)
1. Zhang et al. Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages. 2024. [[arxiv]](https://arxiv.org/abs/2402.12204)
1. Kim et al. Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.14714)
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1. Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.15043)
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1. Huang et al. Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2403.02333)
1. Duan et al. Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization. 2024. [[arxiv]](https://arxiv.org/abs/2403.03419)
1. Xie and Schwertfeger. Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2403.08228)
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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.
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> [!TIP]
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> If you have a project that should be incorporated, please contact via email or create a pull request.
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## License
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This repository is licensed under the [Apache-2.0 License ](LICENSE ).
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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 ) / [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 ](https://ai.meta.com/llama/license/ ) / [Mistral ](LICENSE ) / [OLMo ](LICENSE ) / [Phi-1.5/2 ](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx ) / [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 )
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## Citation
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If this work is helpful, please kindly cite as:
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```bibtex
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@article {zheng2024llamafactory,
title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},
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author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Yongqiang Ma},
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journal={arXiv preprint arXiv:2403.13372},
year={2024},
url={http://arxiv.org/abs/2403.13372}
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}
```
## Acknowledgement
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This repo benefits from [PEFT ](https://github.com/huggingface/peft ), [QLoRA ](https://github.com/artidoro/qlora ) and [FastChat ](https://github.com/lm-sys/FastChat ). Thanks for their wonderful works.
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## Star History
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