646 lines
43 KiB
Markdown
646 lines
43 KiB
Markdown
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[](https://github.com/hiyouga/LLaMA-Factory/stargazers)
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[](LICENSE)
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[](https://github.com/hiyouga/LLaMA-Factory/commits/main)
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[](https://pypi.org/project/llamafactory/)
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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://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)
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[](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
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[](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
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[](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
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[](https://trendshift.io/repositories/4535)
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👋 Join our [WeChat](assets/wechat.jpg) or [NPU user group](assets/wechat_npu.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:
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- **Colab**: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
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- **PAI-DSW**: https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
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- **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)
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- [Changelog](#changelog)
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- [Supported Models](#supported-models)
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- [Supported Training Approaches](#supported-training-approaches)
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- [Provided Datasets](#provided-datasets)
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- [Requirement](#requirement)
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- [Getting Started](#getting-started)
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- [Projects using LLaMA Factory](#projects-using-llama-factory)
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- [License](#license)
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- [Citation](#citation)
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- [Acknowledgement](#acknowledgement)
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## Features
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- **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
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- **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
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- **Scalable resources**: 16-bit full-tuning, freeze-tuning, LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ.
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- **Advanced algorithms**: GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA 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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[24/06/16] We support **[PiSSA](https://arxiv.org/abs/2404.02948)** algorithm. See [examples](examples/README.md) for usage.
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[24/06/07] We supported fine-tuning the **[Qwen2](https://qwenlm.github.io/blog/qwen2/)** and **[GLM-4](https://github.com/THUDM/GLM-4)** models.
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[24/05/26] We supported **[SimPO](https://arxiv.org/abs/2405.14734)** algorithm for preference learning. See [examples](examples/README.md) for usage.
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<details><summary>Full Changelog</summary>
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[24/05/20] We supported fine-tuning the **PaliGemma** series models. Note that the PaliGemma models are pre-trained models, you need to fine-tune them with `gemma` template for chat completion.
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[24/05/18] We supported **[KTO](https://arxiv.org/abs/2402.01306)** algorithm for preference learning. See [examples](examples/README.md) for usage.
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[24/05/14] We supported training and inference on the Ascend NPU devices. Check [installation](#installation) section for details.
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[24/04/26] We supported fine-tuning the **LLaVA-1.5** multimodal LLMs. See [examples](examples/README.md) for usage.
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[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.
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[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](examples/README.md) for usage.
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[24/04/16] We supported **[BAdam](https://arxiv.org/abs/2404.02827)**. See [examples](examples/README.md) for usage.
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[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).
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[24/03/31] We supported **[ORPO](https://arxiv.org/abs/2403.07691)**. See [examples](examples/README.md) for usage.
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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](examples/README.md) for usage.
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[24/03/13] We supported **[LoRA+](https://arxiv.org/abs/2402.12354)**. See [examples](examples/README.md) 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](examples/README.md) 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.
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[24/02/28] We supported weight-decomposed LoRA (**[DoRA](https://arxiv.org/abs/2402.09353)**). Try `use_dora: true` 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](examples/README.md) 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_en`.
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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: true` 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](#download-from-modelscope-hub) 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: 5` argument to activate NEFTune.
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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: true` 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 [examples](examples/README.md) for usage.
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[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.
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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 [examples](examples/README.md) for usage.
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[23/07/31] We supported **dataset streaming**. Try `streaming: true` 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**.
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[23/06/03] We supported quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). See [examples](examples/README.md) for usage.
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</details>
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## Supported Models
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| Model | Model size | Template |
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| ------------------------------------------------------------ | -------------------------------- | --------- |
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| [Baichuan 2](https://huggingface.co/baichuan-inc) | 7B/13B | baichuan2 |
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| [BLOOM/BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | - |
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| [ChatGLM3](https://huggingface.co/THUDM) | 6B | chatglm3 |
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| [Command R](https://huggingface.co/CohereForAI) | 35B/104B | cohere |
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| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | deepseek |
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| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
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| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma |
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| [GLM-4](https://huggingface.co/THUDM) | 9B | glm4 |
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| [InternLM2](https://huggingface.co/internlm) | 7B/20B | intern2 |
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| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
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| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
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| [Llama 3](https://huggingface.co/meta-llama) | 8B/70B | llama3 |
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| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | vicuna |
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| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |
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| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |
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| [PaliGemma](https://huggingface.co/google) | 3B | gemma |
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| [Phi-1.5/Phi-2](https://huggingface.co/microsoft) | 1.3B/2.7B | - |
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| [Phi-3](https://huggingface.co/microsoft) | 4B/7B/14B | phi |
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| [Qwen/Qwen1.5/Qwen2 (Code/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/4B/7B/14B/32B/72B/110B | qwen |
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| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
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| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
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| [Yi/Yi-1.5](https://huggingface.co/01-ai) | 6B/9B/34B | yi |
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| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | yi_vl |
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| [Yuan 2](https://huggingface.co/IEITYuan) | 2B/51B/102B | yuan |
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> [!NOTE]
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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 "instruct/chat" models.
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>
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> Remember to use the **SAME** template in training and inference.
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Please refer to [constants.py](src/llamafactory/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/llamafactory/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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| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
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| Pre-Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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| 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: |
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| PPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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| DPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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| KTO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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| ORPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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| SimPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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## Provided Datasets
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<details><summary>Pre-training datasets</summary>
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- [Wiki Demo (en)](data/wiki_demo.txt)
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- [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
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- [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2)
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- [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220)
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- [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered)
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- [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile)
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- [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B)
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- [FineWeb (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
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- [FineWeb-Edu (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
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- [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack)
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- [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
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</details>
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<details><summary>Supervised fine-tuning datasets</summary>
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- [Identity (en&zh)](data/identity.json)
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- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
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- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3)
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- [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
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- [Glaive Function Calling V2 (en&zh)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
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- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
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- [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
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- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
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- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
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- [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
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- [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
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- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
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- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
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- [UltraChat (en)](https://github.com/thunlp/UltraChat)
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- [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
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- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
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- [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)
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- [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)
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- [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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- [Advertise Generating (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
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- [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)
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- [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
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- [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
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- [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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- [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
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- [STEM (zh)](https://huggingface.co/datasets/hfl/stem_zh_instruction)
|
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- [Ruozhiba (zh)](https://huggingface.co/datasets/hfl/ruozhiba_gpt4_turbo)
|
||
- [Neo-sft (zh)](https://huggingface.co/datasets/m-a-p/neo_sft_phase2)
|
||
- [WebInstructSub (en)](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub)
|
||
- [Magpie-Pro-300K-Filtered (en)](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered)
|
||
- [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)
|
||
|
||
</details>
|
||
|
||
<details><summary>Preference datasets</summary>
|
||
|
||
- [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k)
|
||
- [UltraFeedback (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)
|
||
- [Orca DPO Pairs (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
|
||
- [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
|
||
- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
|
||
- [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)
|
||
- [KTO mixed (en)](https://huggingface.co/datasets/argilla/kto-mix-15k)
|
||
|
||
</details>
|
||
|
||
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 | 3.11 |
|
||
| torch | 1.13.1 | 2.3.0 |
|
||
| transformers | 4.41.2 | 4.41.2 |
|
||
| datasets | 2.16.0 | 2.19.2 |
|
||
| accelerate | 0.30.1 | 0.30.1 |
|
||
| peft | 0.11.1 | 0.11.1 |
|
||
| trl | 0.8.6 | 0.9.4 |
|
||
|
||
| Optional | Minimum | Recommend |
|
||
| ------------ | ------- | --------- |
|
||
| CUDA | 11.6 | 12.2 |
|
||
| deepspeed | 0.10.0 | 0.14.0 |
|
||
| bitsandbytes | 0.39.0 | 0.43.1 |
|
||
| vllm | 0.4.3 | 0.4.3 |
|
||
| flash-attn | 2.3.0 | 2.5.9 |
|
||
|
||
### 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 --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
|
||
cd LLaMA-Factory
|
||
pip install -e ".[torch,metrics]"
|
||
```
|
||
|
||
Extra dependencies available: torch, torch-npu, metrics, deepspeed, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, galore, badam, qwen, modelscope, quality
|
||
|
||
> [!TIP]
|
||
> Use `pip install --no-deps -e .` to resolve package conflicts.
|
||
|
||
<details><summary>For Windows users</summary>
|
||
|
||
If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you need 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.
|
||
|
||
</details>
|
||
|
||
<details><summary>For Ascend NPU users</summary>
|
||
|
||
To install LLaMA Factory on Ascend NPU devices, please specify extra dependencies: `pip install -e ".[torch-npu,metrics]"`. Additionally, you need to install the **[Ascend CANN Toolkit and Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**. Please follow the [installation tutorial](https://www.hiascend.com/document/detail/en/CANNCommunityEdition/600alphaX/softwareinstall/instg/atlasdeploy_03_0031.html) or use the following commands:
|
||
|
||
```bash
|
||
# replace the url according to your CANN version and devices
|
||
# install CANN Toolkit
|
||
wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-toolkit_8.0.RC1.alpha001_linux-"$(uname -i)".run
|
||
bash Ascend-cann-toolkit_8.0.RC1.alpha001_linux-"$(uname -i)".run --install
|
||
|
||
# install CANN Kernels
|
||
wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run
|
||
bash Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run --install
|
||
|
||
# set env variables
|
||
source /usr/local/Ascend/ascend-toolkit/set_env.sh
|
||
```
|
||
|
||
| Requirement | Minimum | Recommend |
|
||
| ------------ | ------- | ----------- |
|
||
| CANN | 8.0.RC1 | 8.0.RC1 |
|
||
| torch | 2.1.0 | 2.1.0 |
|
||
| torch-npu | 2.1.0 | 2.1.0.post3 |
|
||
| deepspeed | 0.13.2 | 0.13.2 |
|
||
|
||
Remember to use `ASCEND_RT_VISIBLE_DEVICES` instead of `CUDA_VISIBLE_DEVICES` to specify the device to use.
|
||
|
||
If you cannot infer model on NPU devices, try setting `do_sample: false` in the configurations.
|
||
|
||
Download the pre-built Docker images: [32GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html) | [64GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
|
||
|
||
</details>
|
||
|
||
### 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
|
||
llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
|
||
llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
|
||
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))
|
||
|
||
```bash
|
||
llamafactory-cli webui
|
||
```
|
||
|
||
### Build Docker
|
||
|
||
For CUDA users:
|
||
|
||
```bash
|
||
cd docker/docker-cuda/
|
||
docker-compose up -d
|
||
docker-compose exec llamafactory bash
|
||
```
|
||
|
||
For Ascend NPU users:
|
||
|
||
```bash
|
||
cd docker/docker-npu/
|
||
docker-compose up -d
|
||
docker-compose exec llamafactory bash
|
||
```
|
||
|
||
<details><summary>Build without Docker Compose</summary>
|
||
|
||
For CUDA users:
|
||
|
||
```bash
|
||
docker build -f ./docker/docker-cuda/Dockerfile \
|
||
--build-arg INSTALL_BNB=false \
|
||
--build-arg INSTALL_VLLM=false \
|
||
--build-arg INSTALL_DEEPSPEED=false \
|
||
--build-arg INSTALL_FLASHATTN=false \
|
||
--build-arg PIP_INDEX=https://pypi.org/simple \
|
||
-t llamafactory:latest .
|
||
|
||
docker run -dit --gpus=all \
|
||
-v ./hf_cache:/root/.cache/huggingface \
|
||
-v ./ms_cache:/root/.cache/modelscope \
|
||
-v ./data:/app/data \
|
||
-v ./output:/app/output \
|
||
-p 7860:7860 \
|
||
-p 8000:8000 \
|
||
--shm-size 16G \
|
||
--name llamafactory \
|
||
llamafactory:latest
|
||
|
||
docker exec -it llamafactory bash
|
||
```
|
||
|
||
For Ascend NPU users:
|
||
|
||
```bash
|
||
# Choose docker image upon your environment
|
||
docker build -f ./docker/docker-npu/Dockerfile \
|
||
--build-arg INSTALL_DEEPSPEED=false \
|
||
--build-arg PIP_INDEX=https://pypi.org/simple \
|
||
-t llamafactory:latest .
|
||
|
||
# Change `device` upon your resources
|
||
docker run -dit \
|
||
-v ./hf_cache:/root/.cache/huggingface \
|
||
-v ./ms_cache:/root/.cache/modelscope \
|
||
-v ./data:/app/data \
|
||
-v ./output:/app/output \
|
||
-v /usr/local/dcmi:/usr/local/dcmi \
|
||
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
|
||
-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
|
||
-v /etc/ascend_install.info:/etc/ascend_install.info \
|
||
-p 7860:7860 \
|
||
-p 8000:8000 \
|
||
--device /dev/davinci0 \
|
||
--device /dev/davinci_manager \
|
||
--device /dev/devmm_svm \
|
||
--device /dev/hisi_hdc \
|
||
--shm-size 16G \
|
||
--name llamafactory \
|
||
llamafactory:latest
|
||
|
||
docker exec -it llamafactory bash
|
||
```
|
||
|
||
</details>
|
||
|
||
<details><summary>Details about volume</summary>
|
||
|
||
- 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.
|
||
|
||
</details>
|
||
|
||
### Deploy with OpenAI-style API and vLLM
|
||
|
||
```bash
|
||
API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
|
||
```
|
||
|
||
> [!TIP]
|
||
> Visit https://platform.openai.com/docs/api-reference/chat/create for API document.
|
||
|
||
### 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`.
|
||
|
||
### Use W&B Logger
|
||
|
||
To use [Weights & Biases](https://wandb.ai) for logging experimental results, you need to add the following arguments to yaml files.
|
||
|
||
```yaml
|
||
report_to: wandb
|
||
run_name: test_run # optional
|
||
```
|
||
|
||
Set `WANDB_API_KEY` to [your key](https://wandb.ai/authorize) when launching training tasks to log in with your W&B account.
|
||
|
||
## Projects using LLaMA Factory
|
||
|
||
If you have a project that should be incorporated, please contact via email or create a pull request.
|
||
|
||
<details><summary>Click to show</summary>
|
||
|
||
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)
|
||
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)
|
||
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)
|
||
1. Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. KDD 2024. [[arxiv]](https://arxiv.org/abs/2401.04319)
|
||
1. Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. ACL 2024. [[arxiv]](https://arxiv.org/abs/2401.07286)
|
||
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. ACL 2024 Findings. [[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)
|
||
1. Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. ACL 2024. [[arxiv]](https://arxiv.org/abs/2402.15043)
|
||
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)
|
||
1. Wu et al. Large Language Models are Parallel Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2403.09073)
|
||
1. Zhang et al. EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling. 2024. [[arxiv]](https://arxiv.org/abs/2403.14541)
|
||
1. Weller et al. FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions. 2024. [[arxiv]](https://arxiv.org/abs/2403.15246)
|
||
1. Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. COLING 2024. [[arxiv]](https://arxiv.org/abs/2403.16008)
|
||
1. Zan et al. CodeS: Natural Language to Code Repository via Multi-Layer Sketch. 2024. [[arxiv]](https://arxiv.org/abs/2403.16443)
|
||
1. Liu et al. Extensive Self-Contrast Enables Feedback-Free Language Model Alignment. 2024. [[arxiv]](https://arxiv.org/abs/2404.00604)
|
||
1. Luo et al. BAdam: A Memory Efficient Full Parameter Training Method for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.02827)
|
||
1. Du et al. Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2404.04167)
|
||
1. Ma et al. Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation. ICML 2024. [[arxiv]](https://arxiv.org/abs/2404.04316)
|
||
1. Liu et al. Dynamic Generation of Personalities with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.07084)
|
||
1. Shang et al. How Far Have We Gone in Stripped Binary Code Understanding Using Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.09836)
|
||
1. Huang et al. LLMTune: Accelerate Database Knob Tuning with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.11581)
|
||
1. Deng et al. Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction. 2024. [[arxiv]](https://arxiv.org/abs/2404.14215)
|
||
1. Acikgoz et al. Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare. 2024. [[arxiv]](https://arxiv.org/abs/2404.16621)
|
||
1. Zhang et al. Small Language Models Need Strong Verifiers to Self-Correct Reasoning. ACL 2024 Findings. [[arxiv]](https://arxiv.org/abs/2404.17140)
|
||
1. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. NAACL 2024. [[arxiv]](https://arxiv.org/abs/2404.18585)
|
||
1. Xu et al. Large Language Models for Cyber Security: A Systematic Literature Review. 2024. [[arxiv]](https://arxiv.org/abs/2405.04760)
|
||
1. Dammu et al. "They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversations. 2024. [[arxiv]](https://arxiv.org/abs/2405.05378)
|
||
1. Yi et al. A safety realignment framework via subspace-oriented model fusion for large language models. 2024. [[arxiv]](https://arxiv.org/abs/2405.09055)
|
||
1. Lou et al. SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling. 2024. [[arxiv]](https://arxiv.org/abs/2405.12739)
|
||
1. Zhang et al. Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2405.13816)
|
||
1. Zhang et al. TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2405.20215)
|
||
1. Zihong Chen. Sentence Segmentation and Sentence Punctuation Based on XunziALLM. 2024. [[paper]](https://aclanthology.org/2024.lt4hala-1.30)
|
||
1. Gao et al. The Best of Both Worlds: Toward an Honest and Helpful Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2406.00380)
|
||
1. Wang and Song. MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset. 2024. [[arxiv]](https://arxiv.org/abs/2406.02106)
|
||
1. Hu et al. Computational Limits of Low-Rank Adaptation (LoRA) for Transformer-Based Models. 2024. [[arxiv]](https://arxiv.org/abs/2406.03136)
|
||
1. Ge et al. Time Sensitive Knowledge Editing through Efficient Finetuning. ACL 2024. [[arxiv]](https://arxiv.org/abs/2406.04496)
|
||
1. Tan et al. Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions. 2024. [[arxiv]](https://arxiv.org/abs/2406.05688)
|
||
1. Song et al. Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters. 2024. [[arxiv]](https://arxiv.org/abs/2406.05955)
|
||
1. Gu et al. RWKV-CLIP: A Robust Vision-Language Representation Learner. 2024. [[arxiv]](https://arxiv.org/abs/2406.06973)
|
||
1. Chen et al. Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees. 2024. [[arxiv]](https://arxiv.org/abs/2406.07115)
|
||
1. Zhu et al. Are Large Language Models Good Statisticians?. 2024. [[arxiv]](https://arxiv.org/abs/2406.07815)
|
||
1. Li et al. Know the Unknown: An Uncertainty-Sensitive Method for LLM Instruction Tuning. 2024. [[arxiv]](https://arxiv.org/abs/2406.10099)
|
||
1. Ding et al. IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce. 2024. [[arxiv]](https://arxiv.org/abs/2406.10173)
|
||
1. He et al. COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities. 2024. [[arxiv]](https://arxiv.org/abs/2406.12074)
|
||
1. Lin et al. FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving. 2024. [[arxiv]](https://arxiv.org/abs/2406.14408)
|
||
1. Treutlein et al. Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. 2024. [[arxiv]](https://arxiv.org/abs/2406.14546)
|
||
1. Feng et al. SS-Bench: A Benchmark for Social Story Generation and Evaluation. 2024. [[arxiv]](https://arxiv.org/abs/2406.15695)
|
||
1. Feng et al. Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement. 2024. [[arxiv]](https://arxiv.org/abs/2406.17233)
|
||
1. Liu et al. Large Language Models for Cuffless Blood Pressure Measurement From Wearable Biosignals. 2024. [[arxiv]](https://arxiv.org/abs/2406.18069)
|
||
1. Iyer et al. Exploring Very Low-Resource Translation with LLMs: The University of Edinburgh’s Submission to AmericasNLP 2024 Translation Task. AmericasNLP 2024. [[paper]](https://aclanthology.org/2024.americasnlp-1.25)
|
||
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/X-D-Lab/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.
|
||
1. **[AutoRE](https://github.com/THUDM/AutoRE)**: A document-level relation extraction system based on large language models.
|
||
1. **[NVIDIA RTX AI Toolkit](https://github.com/NVIDIA/RTX-AI-Toolkit)**: SDKs for fine-tuning LLMs on Windows PC for NVIDIA RTX.
|
||
1. **[LazyLLM](https://github.com/LazyAGI/LazyLLM)**: An easy and lazy way for building multi-agent LLMs applications and supports model fine-tuning via LLaMA Factory.
|
||
|
||
</details>
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||
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||
## License
|
||
|
||
This repository is licensed under the [Apache-2.0 License](LICENSE).
|
||
|
||
Please follow the model licenses to use the corresponding model weights: [Baichuan 2](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) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [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/Phi-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) / [StarCoder 2](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) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
|
||
|
||
## Citation
|
||
|
||
If this work is helpful, please kindly cite as:
|
||
|
||
```bibtex
|
||
@inproceedings{zheng2024llamafactory,
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||
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 Zhangchi Feng and Yongqiang Ma},
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||
booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)},
|
||
address={Bangkok, Thailand},
|
||
publisher={Association for Computational Linguistics},
|
||
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.
|
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|
||
## Star History
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