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Fine-tuning a large language model can be easy as...

https://github.com/user-attachments/assets/7c96b465-9df7-45f4-8053-bf03e58386d3

Choose your path:

• Colab: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
• PAI-DSW: https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
• Local machine: Please refer to usage
• Documentation (WIP): https://llamafactory.readthedocs.io/zh-cn/latest/

Table of Contents

• Features
• Benchmark
• Changelog
• Supported Models
• Supported Training Approaches
• Provided Datasets
• Requirement
• Getting Started
• Projects using LLaMA Factory
• License
• Citation
• Acknowledgement

Features

• Various models: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Qwen2-VL, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
• Integrated methods: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
• 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.
• Advanced algorithms: GaLore, BAdam, Adam-mini, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA and Agent tuning.
• Practical tricks: FlashAttention-2, Unsloth, Liger Kernel, RoPE scaling, NEFTune and rsLoRA.
• Experiment monitors: LlamaBoard, TensorBoard, Wandb, MLflow, etc.
• Faster inference: OpenAI-style API, Gradio UI and CLI with vLLM worker.

Benchmark

Compared to ChatGLM's P-Tuning, 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.

Definitions

- **Training Speed**: the number of training samples processed per second during the training. (bs=4, cutoff_len=1024) - **Rouge Score**: Rouge-2 score on the development set of the [advertising text generation](https://aclanthology.org/D19-1321.pdf) task. (bs=4, cutoff_len=1024) - **GPU Memory**: Peak GPU memory usage in 4-bit quantized training. (bs=1, cutoff_len=1024) - We adopt `pre_seq_len=128` for ChatGLM's P-Tuning and `lora_rank=32` for LLaMA Factory's LoRA tuning.

Changelog

[24/08/30] We support fine-tuning the Qwen2-VL models. Thank @simonJJJ's PR.

[24/08/27] We support Liger Kernel. Try enable_liger_kernel: true for efficient training.

[24/08/09] We support Adam-mini optimizer. See examples for usage. Thank @relic-yuexi's PR.

Full Changelog

[24/07/04] We support [contamination-free packed training](https://github.com/MeetKai/functionary/tree/main/functionary/train/packing). Use `neat_packing: true` to activate it. Thank [@chuan298](https://github.com/chuan298)'s PR. [24/06/16] We support **[PiSSA](https://arxiv.org/abs/2404.02948)** algorithm. See [examples](examples/README.md) for usage. [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. [24/05/26] We supported **[SimPO](https://arxiv.org/abs/2405.14734)** algorithm for preference learning. See [examples](examples/README.md) for usage. [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 `paligemma` template for chat completion. [24/05/18] We supported **[KTO](https://arxiv.org/abs/2402.01306)** algorithm for preference learning. See [examples](examples/README.md) for usage. [24/05/14] We supported training and inference on the Ascend NPU devices. Check [installation](#installation) section for details. [24/04/26] We supported fine-tuning the **LLaVA-1.5** multimodal LLMs. See [examples](examples/README.md) for usage. [24/04/22] We provided a **[Colab notebook](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)** for fine-tuning the Llama-3 model on a free T4 GPU. Two Llama-3-derived models fine-tuned using LLaMA Factory are available at Hugging Face, check [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) and [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese) for details. [24/04/21] We supported **[Mixture-of-Depths](https://arxiv.org/abs/2404.02258)** according to [AstraMindAI's implementation](https://github.com/astramind-ai/Mixture-of-depths). See [examples](examples/README.md) for usage. [24/04/16] We supported **[BAdam](https://arxiv.org/abs/2404.02827)** optimizer. See [examples](examples/README.md) for usage. [24/04/16] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s long-sequence training (Llama-2-7B-56k within 24GB). It achieves **117%** speed and **50%** memory compared with FlashAttention-2, more benchmarks can be found in [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison). [24/03/31] We supported **[ORPO](https://arxiv.org/abs/2403.07691)**. See [examples](examples/README.md) for usage. [24/03/21] Our paper "[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://arxiv.org/abs/2403.13372)" is available at arXiv! [24/03/20] We supported **FSDP+QLoRA** that fine-tunes a 70B model on 2x24GB GPUs. See [examples](examples/README.md) for usage. [24/03/13] We supported **[LoRA+](https://arxiv.org/abs/2402.12354)**. See [examples](examples/README.md) for usage. [24/03/07] We supported **[GaLore](https://arxiv.org/abs/2403.03507)** optimizer. See [examples](examples/README.md) for usage. [24/03/07] We integrated **[vLLM](https://github.com/vllm-project/vllm)** for faster and concurrent inference. Try `infer_backend: vllm` to enjoy **270%** inference speed. [24/02/28] We supported weight-decomposed LoRA (**[DoRA](https://arxiv.org/abs/2402.09353)**). Try `use_dora: true` to activate DoRA training. [24/02/15] We supported **block expansion** proposed by [LLaMA Pro](https://github.com/TencentARC/LLaMA-Pro). See [examples](examples/README.md) for usage. [24/02/05] Qwen1.5 (Qwen2 beta version) series models are supported in LLaMA-Factory. Check this [blog post](https://qwenlm.github.io/blog/qwen1.5/) for details. [24/01/18] We supported **agent tuning** for most models, equipping model with tool using abilities by fine-tuning with `dataset: glaive_toolcall_en`. [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. [23/12/12] We supported fine-tuning the latest MoE model **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)** in our framework. See hardware requirement [here](#hardware-requirement). [23/12/01] We supported downloading pre-trained models and datasets from the **[ModelScope Hub](https://modelscope.cn/models)** for Chinese mainland users. See [this tutorial](#download-from-modelscope-hub) for usage. [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. [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. [23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [examples](examples/README.md) for usage. [23/09/10] We supported **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**. Try `flash_attn: fa2` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs. [23/08/12] We supported **RoPE scaling** to extend the context length of the LLaMA models. Try `rope_scaling: linear` argument in training and `rope_scaling: dynamic` argument at inference to extrapolate the position embeddings. [23/08/11] We supported **[DPO training](https://arxiv.org/abs/2305.18290)** for instruction-tuned models. See [examples](examples/README.md) for usage. [23/07/31] We supported **dataset streaming**. Try `streaming: true` and `max_steps: 10000` arguments to load your dataset in streaming mode. [23/07/29] We released two instruction-tuned 13B models at Hugging Face. See these Hugging Face Repos ([LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/Baichuan-13B-sft)) for details. [23/07/18] We developed an **all-in-one Web UI** for training, evaluation and inference. Try `train_web.py` to fine-tune models in your Web browser. Thank [@KanadeSiina](https://github.com/KanadeSiina) and [@codemayq](https://github.com/codemayq) for their efforts in the development. [23/07/09] We released **[FastEdit](https://github.com/hiyouga/FastEdit)** , an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow [FastEdit](https://github.com/hiyouga/FastEdit) if you are interested. [23/06/29] We provided a **reproducible example** of training a chat model using instruction-following datasets, see [Baichuan-7B-sft](https://huggingface.co/hiyouga/Baichuan-7B-sft) for details. [23/06/22] We aligned the [demo API](src/api_demo.py) with the [OpenAI's](https://platform.openai.com/docs/api-reference/chat) format where you can insert the fine-tuned model in **arbitrary ChatGPT-based applications**. [23/06/03] We supported quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). See [examples](examples/README.md) for usage.

Supported Models

| Model | Model size | Template | | ----------------------------------------------------------------- | -------------------------------- | --------- | | Baichuan 2 | 7B/13B | baichuan2 | | BLOOM/BLOOMZ | 560M/1.1B/1.7B/3B/7.1B/176B | - | | ChatGLM3 | 6B | chatglm3 | | Command R | 35B/104B | cohere | | DeepSeek (Code/MoE) | 7B/16B/67B/236B | deepseek | | Falcon | 7B/11B/40B/180B | falcon | | Gemma/Gemma 2/CodeGemma | 2B/7B/9B/27B | gemma | | GLM-4 | 9B | glm4 | | InternLM2/InternLM2.5 | 7B/20B | intern2 | | Llama | 7B/13B/33B/65B | - | | Llama 2 | 7B/13B/70B | llama2 | | Llama 3/Llama 3.1 | 8B/70B | llama3 | | LLaVA-1.5 | 7B/13B | llava | | MiniCPM | 1B/2B/4B | cpm/cpm3 | | Mistral/Mixtral | 7B/8x7B/8x22B | mistral | | OLMo | 1B/7B | - | | PaliGemma | 3B | paligemma | | Phi-1.5/Phi-2 | 1.3B/2.7B | - | | Phi-3 | 4B/7B/14B | phi | | Qwen/Qwen1.5/Qwen2 (Code/Math/MoE) | 0.5B/1.5B/4B/7B/14B/32B/72B/110B | qwen | | Qwen2-VL | 2B/7B | qwen2vl | | StarCoder 2 | 3B/7B/15B | - | | XVERSE | 7B/13B/65B | xverse | | Yi/Yi-1.5 (Code) | 1.5B/6B/9B/34B | yi | | Yi-VL | 6B/34B | yivl | | Yuan 2 | 2B/51B/102B | yuan |

[!NOTE] For the "base" models, the template argument can be chosen from default, alpaca, vicuna etc. But make sure to use the corresponding template for the "instruct/chat" models.

Remember to use the SAME template in training and inference.

Please refer to constants.py for a full list of models we supported.

You also can add a custom chat template to template.py.

Supported Training Approaches

| Approach | Full-tuning | Freeze-tuning | LoRA | QLoRA | | ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ | | Pre-Training | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | | Supervised Fine-Tuning | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | | Reward Modeling | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | | PPO Training | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | | DPO Training | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | | KTO Training | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | | ORPO Training | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | | SimPO Training | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: |

[!TIP] The implementation details of PPO can be found in this blog.

Provided Datasets

Pre-training datasets

- [Wiki Demo (en)](data/wiki_demo.txt) - [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) - [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2) - [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220) - [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered) - [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile) - [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B) - [FineWeb (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb) - [FineWeb-Edu (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) - [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack) - [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)

Supervised fine-tuning datasets

- [Identity (en&zh)](data/identity.json) - [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca) - [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3) - [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) - [Glaive Function Calling V2 (en&zh)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) - [LIMA (en)](https://huggingface.co/datasets/GAIR/lima) - [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) - [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) - [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) - [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT) - [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca) - [SlimOrca (en)](https://huggingface.co/datasets/Open-Orca/SlimOrca) - [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) - [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M) - [Wiki QA (en)](https://huggingface.co/datasets/wiki_qa) - [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa) - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn) - [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar) - [deepctrl (en&zh)](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data) - [Advertise Generating (zh)](https://huggingface.co/datasets/HasturOfficial/adgen) - [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k) - [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4) - [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) - [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct) - [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) - [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k) - [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) - [STEM (zh)](https://huggingface.co/datasets/hfl/stem_zh_instruction) - [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) - [Magpie-ultra-v0.1 (en)](https://huggingface.co/datasets/argilla/magpie-ultra-v0.1) - [LLaVA mixed (en&zh)](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k) - [Pokemon-gpt4o-captions (en&zh)](https://huggingface.co/datasets/jugg1024/pokemon-gpt4o-captions) - [Open Assistant (de)](https://huggingface.co/datasets/mayflowergmbh/oasst_de) - [Dolly 15k (de)](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de) - [Alpaca GPT4 (de)](https://huggingface.co/datasets/mayflowergmbh/alpaca-gpt4_de) - [OpenSchnabeltier (de)](https://huggingface.co/datasets/mayflowergmbh/openschnabeltier_de) - [Evol Instruct (de)](https://huggingface.co/datasets/mayflowergmbh/evol-instruct_de) - [Dolphin (de)](https://huggingface.co/datasets/mayflowergmbh/dolphin_de) - [Booksum (de)](https://huggingface.co/datasets/mayflowergmbh/booksum_de) - [Airoboros (de)](https://huggingface.co/datasets/mayflowergmbh/airoboros-3.0_de) - [Ultrachat (de)](https://huggingface.co/datasets/mayflowergmbh/ultra-chat_de)

Preference datasets

- [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k) - [UltraFeedback (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) - [RLHF-V (en)](https://huggingface.co/datasets/openbmb/RLHF-V-Dataset) - [VLFeedback (en)](https://huggingface.co/datasets/Zhihui/VLFeedback) - [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)

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 | 2.4.0 | | transformers | 4.41.2 | 4.43.4 | | datasets | 2.16.0 | 2.20.0 | | accelerate | 0.30.1 | 0.32.0 | | peft | 0.11.1 | 0.12.0 | | trl | 0.8.6 | 0.9.6 |

| Optional | Minimum | Recommend | | ------------ | ------- | --------- | | CUDA | 11.6 | 12.2 | | deepspeed | 0.10.0 | 0.14.0 | | bitsandbytes | 0.39.0 | 0.43.1 | | vllm | 0.4.3 | 0.5.0 | | flash-attn | 2.3.0 | 2.6.3 |

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, liger-kernel, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, galore, badam, adam-mini, qwen, modelscope, quality

[!TIP] Use pip install --no-deps -e . to resolve package conflicts.

For Windows users

If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you 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.

For Ascend NPU users

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)

Data Preparation

Please refer to 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 for advanced usage (including distributed training).

[!TIP] Use llamafactory-cli help to show help information.

Fine-Tuning with LLaMA Board GUI (powered by 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

For AMD ROCm users:

bash cd docker/docker-rocm/ docker compose up -d docker compose exec llamafactory bash

Build without Docker Compose

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 ``` For AMD ROCm users: ```bash docker build -f ./docker/docker-rocm/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 \ -v ./hf_cache:/root/.cache/huggingface \ -v ./ms_cache:/root/.cache/modelscope \ -v ./data:/app/data \ -v ./output:/app/output \ -v ./saves:/app/saves \ -p 7860:7860 \ -p 8000:8000 \ --device /dev/kfd \ --device /dev/dri \ --shm-size 16G \ --name llamafactory \ llamafactory:latest docker exec -it llamafactory bash ```

Details about volume

- `hf_cache`: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory. - `ms_cache`: Similar to Hugging Face cache but for ModelScope users. - `data`: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI. - `output`: Set export dir to this location so that the merged result can be accessed directly on the host machine.

Deploy with OpenAI-style API and vLLM

bash API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml

[!TIP] Visit this page 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, e.g., LLM-Research/Meta-Llama-3-8B-Instruct.

Use W&B Logger

To use Weights & Biases 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 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.

Click to show

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. Li et al. Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring. 2024. [[arxiv]](https://arxiv.org/abs/2406.19949) 1. Yang et al. Financial Knowledge Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2407.00365) 1. Lin et al. DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging. 2024. [[arxiv]](https://arxiv.org/abs/2407.01470) 1. Bako et al. Evaluating the Semantic Profiling Abilities of LLMs for Natural Language Utterances in Data Visualization. 2024. [[arxiv]](https://arxiv.org/abs/2407.06129) 1. Huang et al. RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization. 2024. [[arxiv]](https://arxiv.org/abs/2407.08044) 1. Jiang et al. LLM-Collaboration on Automatic Science Journalism for the General Audience. 2024. [[arxiv]](https://arxiv.org/abs/2407.09756) 1. Inouye et al. Applied Auto-tuning on LoRA Hyperparameters. 2024. [[paper]](https://scholarcommons.scu.edu/cseng_senior/272/) 1. Qi et al. Research on Tibetan Tourism Viewpoints information generation system based on LLM. 2024. [[arxiv]](https://arxiv.org/abs/2407.13561) 1. Xu et al. Course-Correction: Safety Alignment Using Synthetic Preferences. 2024. [[arxiv]](https://arxiv.org/abs/2407.16637) 1. Sun et al. LAMBDA: A Large Model Based Data Agent. 2024. [[arxiv]](https://arxiv.org/abs/2407.17535) 1. Zhu et al. CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in Healthcare. 2024. [[arxiv]](https://arxiv.org/abs/2407.19705) 1. Yu et al. Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment. 2024. [[arxiv]](https://arxiv.org/abs/2408.00137) 1. Xie et al. The Power of Personalized Datasets: Advancing Chinese Composition Writing for Elementary School through Targeted Model Fine-Tuning. IALP 2024. [[paper]](https://www.asianlp.sg/conferences/ialp2024/proceedings/papers/IALP2024_P055.pdf) 1. Liu et al. Instruct-Code-Llama: Improving Capabilities of Language Model in Competition Level Code Generation by Online Judge Feedback. ICIC 2024. [[paper]](https://link.springer.com/chapter/10.1007/978-981-97-5669-8_11) 1. Wang et al. Cybernetic Sentinels: Unveiling the Impact of Safety Data Selection on Model Security in Supervised Fine-Tuning. ICIC 2024. [[paper]](https://link.springer.com/chapter/10.1007/978-981-97-5669-8_23) 1. Xia et al. Understanding the Performance and Estimating the Cost of LLM Fine-Tuning. 2024. [[arxiv]](https://arxiv.org/abs/2408.04693) 1. Zeng et al. Perceive, Reflect, and Plan: Designing LLM Agent for Goal-Directed City Navigation without Instructions. 2024. [[arxiv]](https://arxiv.org/abs/2408.04168) 1. Xia et al. Using Pre-trained Language Model for Accurate ESG Prediction. FinNLP 2024. [[paper]](https://aclanthology.org/2024.finnlp-2.1/) 1. Liang et al. I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm. 2024. [[arxiv]](https://arxiv.org/abs/2408.08072) 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.

License

This repository is licensed under the Apache-2.0 License.

Please follow the model licenses to use the corresponding model weights: Baichuan 2 / BLOOM / ChatGLM3 / Command R / DeepSeek / Falcon / Gemma / GLM-4 / InternLM2 / Llama / Llama 2 (LLaVA-1.5) / Llama 3 / MiniCPM / Mistral / OLMo / Phi-1.5/Phi-2 / Phi-3 / Qwen / StarCoder 2 / XVERSE / Yi / Yi-1.5 / Yuan 2

Citation

If this work is helpful, please kindly cite as:

bibtex @inproceedings{zheng2024llamafactory, title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models}, author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Zhangchi Feng and Yongqiang Ma}, 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, TRL, QLoRA and FastChat. Thanks for their wonderful works.

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