hiyouga/LlamaFactory
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Easily fine-tune 100+ large language models with zero-code CLI and Web UI
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[ English | 中文 ]
Fine-tuning a large language model can be easy as...
https://github.com/user-attachments/assets/3991a3a8-4276-4d30-9cab-4cb0c4b9b99e
Start local training:
- Please refer to usage
Start cloud training:
- Colab (free): https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
- PAI-DSW (free trial): https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
- AMD GPU Cloud (free credits): https://github.com/AMD-AIM/AMD_Developers_Notebooks/blob/main/en/AMD_developer_LLaMAFactory_note_en.md
Read technical notes:
- Documentation (WIP): https://llamafactory.readthedocs.io/en/latest/
- Documentation (AMD GPU): https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/fine_tune/llama_factory_llama3.html
- Documentation (ASCEND NPU): https://llamafactory.readthedocs.io/en/latest/multibackend/npu/index.html
- Official Blog: https://blog.llamafactory.net/en/
[!NOTE] Except for the above links, all other websites are unauthorized third-party websites. Please carefully use them.
Table of Contents
- Features
- Blogs
- Changelog
- Supported Models
- Supported Training Approaches
- Provided Datasets
- Requirement
- Getting Started
- Installation
- Data Preparation
- Quickstart
- Fine-Tuning with LLaMA Board GUI
- Build Docker
- Deploy with OpenAI-style API and vLLM
- Download from ModelScope Hub
- Download from Modelers Hub
- Use W&B Logger
- Use SwanLab Logger
- Projects using LLaMA Factory
- License
- Citation
- Acknowledgement
Features
- Various models: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen3, Qwen3-VL, DeepSeek, Gemma, GLM, 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, APOLLO, Adam-mini, Muon, OFT, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and PiSSA.
- Practical tricks: FlashAttention-2, Unsloth, Liger Kernel, KTransformers, RoPE scaling, NEFTune and rsLoRA.
- Wide tasks: Multi-turn dialogue, tool using, image understanding, visual grounding, video recognition, audio understanding, etc.
- Experiment monitors: LlamaBoard, TensorBoard, Wandb, MLflow, SwanLab, etc.
- Faster inference: OpenAI-style API, Gradio UI and CLI with vLLM worker or SGLang worker.
Day-N Support for Fine-Tuning Cutting-Edge Models
| Support Date | Model Name |
|---|---|
| Day 0 | Qwen3 / Qwen2.5-VL / Gemma 3 / GLM-4.1V / InternLM 3 / MiniCPM-o-2.6 |
| Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 / Llama 4 |
Blogs
[!TIP] Now we have a dedicated blog for LLaMA Factory!
Website: https://blog.llamafactory.net/en/
- 💡 KTransformers Fine-Tuning × LLaMA Factory: Fine-tuning 1000 Billion models with 2 4090-GPU + CPU (English)
- 💡 Easy Dataset × LLaMA Factory: Enabling LLMs to Efficiently Learn Domain Knowledge (English)
- 💡 DataFlow × LLaMA Factory: Producing High-Quality Data for LLM Training with a Data Preparation Pipeline (English) | 中文
- 💡 DataFlex × LLaMA Factory: A Data-Centric Dynamic Training System Built on LLaMA-Factory (English) | 中文
- A One-Stop Code-Free Model Reinforcement Learning and Deployment Platform based on LLaMA-Factory and EasyR1 (Chinese)
- How Apoidea Group enhances visual information extraction from banking documents with multimodal models using LLaMA-Factory on Amazon SageMaker HyperPod (English)
All Blogs
- LLaMA Factory: Fine-tuning the DeepSeek-R1-Distill-Qwen-7B Model for News Classifier (Chinese)
- A One-Stop Code-Free Model Fine-Tuning & Deployment Platform based on SageMaker and LLaMA-Factory (Chinese)
- LLaMA Factory Multi-Modal Fine-Tuning Practice: Fine-Tuning Qwen2-VL for Personal Tourist Guide (Chinese)
- LLaMA Factory: Fine-tuning Llama3 for Role-Playing (Chinese)
Changelog
[25/10/26] We support Megatron-core training backend with mcore_adapter. See PR #9237 to get started.
[25/08/22] We supported OFT and OFTv2. See examples for usage.
[25/08/20] We supported fine-tuning the Intern-S1-mini models. See PR #8976 to get started.
[25/08/06] We supported fine-tuning the GPT-OSS models. See PR #8826 to get started.
Full Changelog
[25/07/02] We supported fine-tuning the GLM-4.1V-9B-Thinking model.
[25/04/28] We supported fine-tuning the Qwen3 model family.
[25/04/21] We supported the Muon optimizer. See examples for usage. Thank @tianshijing's PR.
[25/04/16] We supported fine-tuning the InternVL3 model. See PR #7258 to get started.
[25/04/14] We supported fine-tuning the GLM-Z1 and Kimi-VL models.
[25/04/06] We supported fine-tuning the Llama 4 model. See PR #7611 to get started.
[25/03/31] We supported fine-tuning the Qwen2.5 Omni model. See PR #7537 to get started.
[25/03/15] We supported SGLang as inference backend. Try infer_backend: sglang to accelerate inference.
[25/03/12] We supported fine-tuning the Gemma 3 model.
[25/02/24] Announcing EasyR1, an efficient, scalable and multi-modality RL training framework for efficient GRPO training.
[25/02/11] We supported saving the Ollama modelfile when exporting the model checkpoints. See examples for usage.
[25/02/05] We supported fine-tuning the Qwen2-Audio and MiniCPM-o-2.6 on audio understanding tasks.
[25/01/31] We supported fine-tuning the DeepSeek-R1 and Qwen2.5-VL models.
[25/01/15] We supported APOLLO optimizer. See examples for usage.
[25/01/14] We supported fine-tuning the MiniCPM-o-2.6 and MiniCPM-V-2.6 models. Thank @BUAADreamer's PR.
[25/01/14] We supported fine-tuning the InternLM 3 models. Thank @hhaAndroid's PR.
[25/01/10] We supported fine-tuning the Phi-4 model.
[24/12/21] We supported using SwanLab for experiment tracking and visualization. See this section for details.
[24/11/27] We supported fine-tuning the Skywork-o1 model and the OpenO1 dataset.
[24/10/09] We supported downloading pre-trained models and datasets from the Modelers Hub. See this tutorial for usage.
[24/09/19] We supported fine-tuning the Qwen2.5 models.
[24/08/30] We supported fine-tuning the Qwen2-VL models. Thank @simonJJJ's PR.
[24/08/27] We supported Liger Kernel. Try enable_liger_kernel: true for efficient training.
[24/08/09] We supported Adam-mini optimizer. See examples for usage. Thank @relic-yuexi's PR.
[24/07/04] We supported contamination-free packed training. Use neat_packing: true to activate it. Thank @chuan298's PR.
[24/06/16] We supported PiSSA algorithm. See examples for usage.
[24/06/07] We supported fine-tuning the Qwen2 and GLM-4 models.
[24/05/26] We supported SimPO algorithm for preference learning. See examples 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 algorithm for preference learning. See examples for usage.
[24/05/14] We supported training and inference on the Ascend NPU devices. Check installation section for details.
[24/04/26] We supported fine-tuning the LLaVA-1.5 multimodal LLMs. See examples for usage.
[24/04/22] We provided a Colab notebook 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 and Llama3-Chinese for details.
[24/04/21] We supported Mixture-of-Depths according to AstraMindAI's implementation. See examples for usage.
[24/04/16] We supported BAdam optimizer. See examples for usage.
[24/04/16] We supported 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.
[24/03/31] We supported ORPO. See examples for usage.
[24/03/21] Our paper "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models" is available at arXiv!
[24/03/20] We supported FSDP+QLoRA that fine-tunes a 70B model on 2x24GB GPUs. See examples for usage.
[24/03/13] We supported LoRA+. See examples for usage.
[24/03/07] We supported GaLore optimizer. See examples for usage.
[24/03/07] We integrated vLLM for faster and concurrent inference. Try infer_backend: vllm to enjoy 270% inference speed.
[24/02/28] We supported weight-decomposed LoRA (DoRA). Try use_dora: true to activate DoRA training.
[24/02/15] We supported block expansion proposed by LLaMA Pro. See examples for usage.
[24/02/05] Qwen1.5 (Qwen2 beta version) series models are supported in LLaMA-Factory. Check this blog post 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'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 for details.
[23/12/12] We supported fine-tuning the latest MoE model Mixtral 8x7B in our framework. See hardware requirement here.
[23/12/01] We supported downloading pre-trained models and datasets from the ModelScope Hub. See this tutorial for usage.
[23/10/21] We supported NEFTune trick for fine-tuning. Try neftune_noise_alpha: 5 argument to activate NEFTune.
[23/09/27] We supported $S^2$-Attn proposed by 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 for usage.
[23/09/10] We supported FlashAttention-2. 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 for instruction-tuned models. See examples 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 / Baichuan) 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 and @codemayq for their efforts in the development.
[23/07/09] We released FastEdit ⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow 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 for details.
[23/06/22] We aligned the demo API with the OpenAI's 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). See examples for usage.
[!TIP] If you cannot use the latest feature, please pull the latest code and install LLaMA-Factory again.
Supported Models
| Model | Model size | Template |
|---|---|---|
| BLOOM/BLOOMZ | 560M/1.1B/1.7B/3B/7.1B/176B | - |
| DeepSeek (LLM/Code/MoE) | 7B/16B/67B/236B | deepseek |
| DeepSeek 3-3.2 | 236B/671B | deepseek3 |
| DeepSeek R1 (Distill) | 1.5B/7B/8B/14B/32B/70B/671B | deepseekr1 |
| ERNIE-4.5 | 0.3B/21B/300B | ernie_nothink |
| Falcon/Falcon H1 | 0.5B/1.5B/3B/7B/11B/34B/40B/180B | falcon/falcon_h1 |
| Gemma/Gemma 2/CodeGemma | 2B/7B/9B/27B | gemma/gemma2 |
| Gemma 3/Gemma 3n | 270M/1B/4B/6B/8B/12B/27B | gemma3/gemma3n |
| GLM-4/GLM-4-0414/GLM-Z1 | 9B/32B | glm4/glmz1 |
| GLM-4.5/GLM-4.5(6)V | 9B/106B/355B | glm4_moe/glm4_5v |
| GPT-2 | 0.1B/0.4B/0.8B/1.5B | - |
| GPT-OSS | 20B/120B | gpt_oss |
| Granite 3-4 | 1B/2B/3B/7B/8B | granite3/granite4 |
| Hunyuan/Hunyuan1.5 (MT) | 0.5B/1.8B/4B/7B/13B | hunyuan/hunyuan_small |
| InternLM 2-3 | 7B/8B/20B | intern2 |
| InternVL 2.5-3.5 | 1B/2B/4B/8B/14B/30B/38B/78B/241B | intern_vl |
| Intern-S1-mini | 8B | intern_s1 |
| Kimi-VL | 16B | kimi_vl |
| Ling 2.0 (mini/flash) | 16B/100B | bailing_v2 |
| LFM 2.5 (VL) | 1.2B/1.6B | lfm2/lfm2_vl |
| Llama | 7B/13B/33B/65B | - |
| Llama 2 | 7B/13B/70B | llama2 |
| Llama 3-3.3 | 1B/3B/8B/70B | llama3 |
| Llama 4 | 109B/402B | llama4 |
| Llama 3.2 Vision | 11B/90B | mllama |
| LLaVA-1.5 | 7B/13B | llava |
| LLaVA-NeXT | 7B/8B/13B/34B/72B/110B | llava_next |
| LLaVA-NeXT-Video | 7B/34B | llava_next_video |
| MiMo | 7B/309B | mimo/mimo_v2 |
| MiniCPM 4/5 | 0.5B/1B/8B | cpm4/empty |
| MiniCPM-o/MiniCPM-V 4.5 | 8B/9B | minicpm_o/minicpm_v |
| MiniCPM-V 4.6 | 3B/8B | minicpm_v_4_6 |
| MiniMax-M1/MiniMax-M2 | 229B/456B | minimax1/minimax2 |
| Ministral 3 | 3B/8B/14B | ministral3 |
| Mistral/Mixtral | 7B/8x7B/8x22B | mistral |
| PaliGemma/PaliGemma2 | 3B/10B/28B | paligemma |
| Phi-3/Phi-3.5 | 4B/14B | phi |
| Phi-3-small | 7B | phi_small |
| Phi-4-mini/Phi-4 | 3.8B/14B | phi4_mini/phi4 |
| Pixtral | 12B | pixtral |
| Qwen2 (Code/Math/MoE/QwQ) | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen |
| Qwen3 (MoE/Instruct/Thinking/Next) | 0.6B/1.7B/4B/8B/14B/32B/80B/235B | qwen3/qwen3_nothink |
| Qwen3.5 | 0.8B/2B/4B/9B/27B/35B/122B/397B | qwen3_5/qwen3_5_nothink |
| Qwen3.6 | 27B/35B | qwen3_6 |
| Qwen2-Audio | 7B | qwen2_audio |
| Qwen2.5-Omni | 3B/7B | qwen2_omni |
| Qwen3-Omni | 30B | qwen3_omni |
| Qwen2-VL/Qwen2.5-VL/QVQ | 2B/3B/7B/32B/72B | qwen2_vl |
| Qwen3-VL | 2B/4B/8B/30B/32B/235B | qwen3_vl |
| Seed (OSS/Coder) | 8B/36B | seed_oss/seed_coder |
| StarCoder 2 | 3B/7B/15B | - |
| TeleChat 2-2.5 | 3B/7B/35B/115B | telechat2 |
| Yuan 2 | 2B/51B/102B | yuan |
[!NOTE] For the "base" models, the
templateargument can be chosen fromdefault,alpaca,vicunaetc. But make sure to use the corresponding template for the "instruct/chat" models.If the model has both reasoning and non-reasoning versions, please use the
_nothinksuffix to distinguish between them. For example,qwen3andqwen3_nothink.Remember to use the SAME template in training and inference.
*: You should install the
transformersfrom main branch and useDISABLE_VERSION_CHECK=1to skip version check.**: You need to install a specific version of
transformersto use the corresponding model.
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 | OFT | QOFT |
|---|---|---|---|---|---|---|
| Pre-Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| Supervised Fine-Tuning | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| Reward Modeling | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| PPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| DPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| KTO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| ORPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| SimPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
[!TIP] The implementation details of PPO can be found in this blog.
Provided Datasets
Pre-training datasets
- Wiki Demo (en)
- RefinedWeb (en)
- RedPajama V2 (en)
- Wikipedia (en)
- Wikipedia (zh)
- Pile (en)
- SkyPile (zh)
- FineWeb (en)
- FineWeb-Edu (en)
- CCI3-HQ (zh)
- CCI3-Data (zh)
- CCI4.0-M2-Base-v1 (en&zh)
- CCI4.0-M2-CoT-v1 (en&zh)
- CCI4.0-M2-Extra-v1 (en&zh)
- The Stack (en)
- StarCoder (en)
Supervised fine-tuning datasets
- Identity (en&zh)
- Stanford Alpaca (en)
- Stanford Alpaca (zh)
- Alpaca GPT4 (en&zh)
- Glaive Function Calling V2 (en&zh)
- LIMA (en)
- Guanaco Dataset (multilingual)
- BELLE 2M (zh)
- BELLE 1M (zh)
- BELLE 0.5M (zh)
- BELLE Dialogue 0.4M (zh)
- BELLE School Math 0.25M (zh)
- BELLE Multiturn Chat 0.8M (zh)
- UltraChat (en)
- OpenPlatypus (en)
- CodeAlpaca 20k (en)
- Alpaca CoT (multilingual)
- OpenOrca (en)
- SlimOrca (en)
- MathInstruct (en)
- Firefly 1.1M (zh)
- Wiki QA (en)
- Web QA (zh)
- WebNovel (zh)
- Nectar (en)
- deepctrl (en&zh)
- Advertise Generating (zh)
- ShareGPT Hyperfiltered (en)
- ShareGPT4 (en&zh)
- UltraChat 200k (en)
- Infinity Instruct (zh)
- AgentInstruct (en)
- LMSYS Chat 1M (en)
- Evol Instruct V2 (en)
- Cosmopedia (en)
- STEM (zh)
- Ruozhiba (zh)
- Neo-sft (zh)
- Magpie-Pro-300K-Filtered (en)
- Magpie-ultra-v0.1 (en)
- WebInstructSub (en)
- OpenO1-SFT (en&zh)
- Open-Thoughts (en)
- Open-R1-Math (en)
- Chinese-DeepSeek-R1-Distill (zh)
- LLaVA mixed (en&zh)
- Pokemon-gpt4o-captions (en&zh)
- DLR-Web (en)
- Open Assistant (de)
- Dolly 15k (de)
- Alpaca GPT4 (de)
- OpenSchnabeltier (de)
- Evol Instruct (de)
- Dolphin (de)
- Booksum (de)
- Airoboros (de)
- Ultrachat (de)
Preference datasets
- DPO mixed (en&zh)
- UltraFeedback (en)
- COIG-P (zh)
- RLHF-V (en)
- VLFeedback (en)
- RLAIF-V (en)
- Orca DPO Pairs (en)
- HH-RLHF (en)
- Nectar (en)
- Orca DPO (de)
- KTO mixed (en)
Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands.
pip install "huggingface_hub<1.0.0"
huggingface-cli login
Requirement
| Mandatory | Minimum | Recommend |
|---|---|---|
| python | 3.11 | >=3.11 |
| torch | 2.0.0 | 2.6.0 |
| torchvision | 0.15.0 | 0.21.0 |
| transformers | 4.49.0 | 4.50.0 |
| datasets | 2.16.0 | 3.2.0 |
| accelerate | 0.34.0 | 1.2.1 |
| peft | 0.14.0 | 0.15.1 |
| trl | 0.8.6 | 0.9.6 |
| Optional | Minimum | Recommend |
|---|---|---|
| CUDA | 11.6 | 12.2 |
| deepspeed | 0.10.0 | 0.16.4 |
| bitsandbytes | 0.39.0 | 0.43.1 |
| vllm | 0.4.3 | 0.8.2 |
| flash-attn | 2.5.6 | 2.7.2 |
Hardware Requirement
* estimated
| Method | Bits | 7B | 14B | 30B | 70B | xB |
|---|---|---|---|---|---|---|
Full (bf16 or fp16) | 32 | 120GB | 240GB | 600GB | 1200GB | 18xGB |
Full (pure_bf16) | 16 | 60GB | 120GB | 300GB | 600GB | 8xGB |
| Freeze/LoRA/GaLore/APOLLO/BAdam/OFT | 16 | 16GB | 32GB | 64GB | 160GB | 2xGB |
| QLoRA / QOFT | 8 | 10GB | 20GB | 40GB | 80GB | xGB |
| QLoRA / QOFT | 4 | 6GB | 12GB | 24GB | 48GB | x/2GB |
| QLoRA / QOFT | 2 | 4GB | 8GB | 16GB | 24GB | x/4GB |
Getting Started
Installation
[!IMPORTANT] Installation is mandatory.
Install from Source
git clone --depth 1 https://github.com/hiyouga/LlamaFactory.git
cd LlamaFactory
pip install -e .
pip install -r requirements/metrics.txt
Optional dependencies available: metrics, deepspeed. Install with: pip install -e . && pip install -r requirements/metrics.txt -r requirements/deepspeed.txt
Additional dependencies for specific features are available in examples/requirements/.
Install from Docker Image
docker run -it --rm --gpus=all --ipc=host hiyouga/llamafactory:latest
This image is built on Ubuntu 22.04 (x86_64), CUDA 12.4, Python 3.11, PyTorch 2.6.0, and Flash-attn 2.7.4.
Find the pre-built images: https://hub.docker.com/r/hiyouga/llamafactory/tags
Please refer to build docker to build the image yourself.
Setting up a virtual environment with uv
Create an isolated Python environment with uv:
uv run llamafactory-cli webui
For Windows users
Install PyTorch
You need to manually install the GPU version of PyTorch on the Windows platform. Please refer to the official website and the following command to install PyTorch with CUDA support:
pip uninstall torch torchvision torchaudio
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
python -c "import torch; print(torch.cuda.is_available())"
If you see True then you have successfully installed PyTorch with CUDA support.
Try dataloader_num_workers: 0 if you encounter Can't pickle local object error.
Install BitsAndBytes
To enable Quantized LoRA (QLoRA) on Windows, you need to install bitsandbytes.
For most users, it is recommended to install the latest official release:
pip install bitsandbytes
If you are using uv to manage your virtual environment, it is recommended to install bitsandbytes after installing the GPU-enabled version of PyTorch:
uv pip install bitsandbytes --no-deps
[!IMPORTANT] Pay attention to the CUDA Toolkit version when installing bitsandbytes. Official bitsandbytes releases are built for specific CUDA Toolkit versions. On Windows x86-64, separate builds are currently provided for CUDA 11.8–12.6 and CUDA 12.8–12.9. Support for NVIDIA RTX 50 Series GPUs (e.g., RTX 5060 Ti, sm_120) requires the CUDA 12.8–12.9 builds.
If your environment uses an older CUDA version, or you need compatibility with older Windows / PyTorch combinations, you can install the third-party precompiled Windows wheel:
pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl
Install Flash Attention-2
To enable FlashAttention-2 on the Windows platform, please use the script from flash-attention-windows-wheel to compile and install it by yourself.
For Ascend NPU users
To install LLaMA Factory on Ascend NPU devices, please upgrade Python to version 3.10 or higher: pip install -r requirements/npu.txt. Additionally, you need to install the Ascend CANN Toolkit and Kernels. Please follow the installation tutorial.
You can also download the pre-built Docker images:
# Docker Hub
docker pull hiyouga/llamafactory:latest-cann9.0.0-torch_npu2.7.1-A2-ubuntu-py3.11
docker pull hiyouga/llamafactory:latest-cann9.0.0-torch_npu2.7.1-A3-ubuntu-py3.11
docker pull hiyouga/llamafactory:latest-cann9.0.0-torch_npu2.7.1-A2-openeuler-py3.11
docker pull hiyouga/llamafactory:latest-cann9.0.0-torch_npu2.7.1-A3-openeuler-py3.11
# quay.io
docker pull quay.io/ascend/llamafactory:latest-cann9.0.0-torch_npu2.7.1-A2-ubuntu-py3.11
docker pull quay.io/ascend/llamafactory:latest-cann9.0.0-torch_npu2.7.1-A3-ubuntu-py3.11
docker pull quay.io/ascend/llamafactory:latest-cann9.0.0-torch_npu2.7.1-A2-openeuler-py3.11
docker pull quay.io/ascend/llamafactory:latest-cann9.0.0-torch_npu2.7.1-A3-openeuler-py3.11
Install BitsAndBytes
To use QLoRA based on bitsandbytes on Ascend NPU, please follow these 3 steps:
- Manually compile bitsandbytes: Refer to the installation documentation for the NPU version of bitsandbytes to complete the compilation and installation. The compilation requires a cmake version of at least 3.22.1 and a g++ version of at least 12.x.
# Install bitsandbytes from source
# Clone bitsandbytes repo, Ascend NPU backend is currently enabled on multi-backend-refactor branch
git clone -b multi-backend-refactor https://github.com/bitsandbytes-foundation/bitsandbytes.git
cd bitsandbytes/
# Install dependencies
pip install -r requirements-dev.txt
# Install the dependencies for the compilation tools. Note that the commands for this step may vary depending on the operating system. The following are provided for reference
apt-get install -y build-essential cmake
# Compile & install
cmake -DCOMPUTE_BACKEND=npu -S .
make
pip install .
- Install transformers from the main branch.
git clone -b main https://github.com/huggingface/transformers.git
cd transformers
pip install .
- Set
double_quantization: falsein the configuration. You can refer to the example.
Data Preparation
Please refer to data/README.md for checking the details about the format of dataset files. You can use datasets on HuggingFace / ModelScope / Modelers hub, load the dataset in local disk, or specify a path to s3/gcs cloud storage.
[!NOTE] Please update
data/dataset_info.jsonto use your custom dataset.
You can also use Easy Dataset, DataFlow and GraphGen to create synthetic data for fine-tuning.
Quickstart
Use the following 3 commands to run LoRA fine-tuning, inference and merging of the Qwen3-4B-Instruct model, respectively.
llamafactory-cli train examples/train_lora/qwen3_lora_sft.yaml
llamafactory-cli chat examples/inference/qwen3_lora_sft.yaml
llamafactory-cli export examples/merge_lora/qwen3_lora_sft.yaml
See examples/README.md for advanced usage (including distributed training).
[!TIP] Use
llamafactory-cli helpto show help information.Read FAQs first if you encounter any problems.
Fine-Tuning with LLaMA Board GUI (powered by Gradio)
llamafactory-cli webui
Build Docker
For CUDA users:
cd docker/docker-cuda/
docker compose up -d
docker compose exec llamafactory bash
For Ascend NPU users (A2 with Ubuntu by default):
cd docker/docker-npu/
docker compose up -d llamafactory-a2-ubuntu
docker compose exec llamafactory-a2-ubuntu bash
Other NPU variants can be started with their corresponding profiles and services:
# A3 with Ubuntu
docker compose --profile a3 up -d llamafactory-a3-ubuntu
docker compose exec llamafactory-a3-ubuntu bash
# A2 with openEuler
docker compose --profile openeuler up -d llamafactory-a2-openeuler
docker compose exec llamafactory-a2-openeuler bash
# A3 with openEuler
docker compose --profile a3-openeuler up -d llamafactory-a3-openeuler
docker compose exec llamafactory-a3-openeuler bash
For AMD ROCm users:
cd docker/docker-rocm/
docker compose up -d
docker compose exec llamafactory bash
Build without Docker Compose
For CUDA users:
docker build -f ./docker/docker-cuda/Dockerfile \
--build-arg PIP_INDEX=https://pypi.org/simple \
-t llamafactory:latest .
docker run -dit --ipc=host --gpus=all \
-p 7860:7860 \
-p 8000:8000 \
--name llamafactory \
llamafactory:latest
docker exec -it llamafactory bash
For Ascend NPU users:
docker build -f ./docker/docker-npu/Dockerfile \
--build-arg PIP_INDEX=https://pypi.org/simple \
-t llamafactory:latest .
docker run -dit --ipc=host \
-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 \
--name llamafactory \
llamafactory:latest
docker exec -it llamafactory bash
For AMD ROCm users:
docker build -f ./docker/docker-rocm/Dockerfile \
--build-arg PIP_INDEX=https://pypi.org/simple \
-t llamafactory:latest .
docker run -dit --ipc=host \
-p 7860:7860 \
-p 8000:8000 \
--device /dev/kfd \
--device /dev/dri \
--name llamafactory \
llamafactory:latest
docker exec -it llamafactory bash
Use Docker volumes
You can uncomment VOLUME [ "/root/.cache/huggingface", "/app/shared_data", "/app/output" ] in the Dockerfile to use data volumes.
When building the Docker image, use -v ./hf_cache:/root/.cache/huggingface argument to mount the local directory to the container. The following data volumes are available.
hf_cache: Utilize Hugging Face cache on the host machine.shared_data: The directionary to store datasets on the host machine.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
API_PORT=8000 llamafactory-cli api examples/inference/qwen3.yaml infer_backend=vllm vllm_enforce_eager=true
[!TIP] Visit this page for API document.
Examples: Image understanding | Function calling
Download from ModelScope Hub
If you have trouble with downloading models and datasets from Hugging Face, you can use ModelScope.
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.
Download from Modelers Hub
You can also use Modelers Hub to download models and datasets.
export USE_OPENMIND_HUB=1 # `set USE_OPENMIND_HUB=1` for Windows
Train the model by specifying a model ID of the Modelers Hub as the model_name_or_path. You can find a full list of model IDs at Modelers Hub, e.g., TeleAI/TeleChat-7B-pt.
Use W&B Logger
To use Weights & Biases for logging experimental results, you need to add the following arguments to yaml files.
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.
Use SwanLab Logger
To use SwanLab for logging experimental results, you need to add the following arguments to yaml files.
use_swanlab: true
swanlab_run_name: test_run # optional
When launching training tasks, you can log in to SwanLab in three ways:
- Add
swanlab_api_key=<your_api_key>to the yaml file, and set it to your API key. - Set the environment variable
SWANLAB_API_KEYto your API key. - Use the
swanlab logincommand to complete the login.
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
- Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [arxiv]
- Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [arxiv]
- Wang et al. UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language. 2023. [arxiv]
- Luceri et al. Leveraging Large Language Models to Detect Influence Campaigns in Social Media. 2023. [arxiv]
- Zhang et al. Alleviating Hallucinations of Large Language Models through Induced Hallucinations. 2023. [arxiv]
- Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. KDD 2024. [arxiv]
- Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. ACL 2024. [arxiv]
- Choi et al. FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs. 2024. [arxiv]
- Zhang et al. AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts. 2024. [arxiv]
- Lyu et al. KnowTuning: Knowledge-aware Fine-tuning for Large Language Models. 2024. [arxiv]
- Yang et al. LaCo: Large Language Model Pruning via Layer Collapse. 2024. [arxiv]
- Bhardwaj et al. Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic. 2024. [arxiv]
- Yang et al. Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models. 2024. [arxiv]
- Yi et al. Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding. ACL 2024 Findings. [arxiv]
- Cao et al. Head-wise Shareable Attention for Large Language Models. 2024. [arxiv]
- Zhang et al. Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages. 2024. [arxiv]
- Kim et al. Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models. 2024. [arxiv]
- Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. ACL 2024. [arxiv]
- Huang et al. Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning. 2024. [arxiv]
- Duan et al. Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization. 2024. [arxiv]
- Xie and Schwertfeger. Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs. 2024. [arxiv]
- Wu et al. Large Language Models are Parallel Multilingual Learners. 2024. [arxiv]
- Zhang et al. EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling. 2024. [arxiv]
- Weller et al. FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions. 2024. [arxiv]
- Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. COLING 2024. [arxiv]
- Zan et al. CodeS: Natural Language to Code Repository via Multi-Layer Sketch. 2024. [arxiv]
- Liu et al. Extensive Self-Contrast Enables Feedback-Free Language Model Alignment. 2024. [arxiv]
- Luo et al. BAdam: A Memory Efficient Full Parameter Training Method for Large Language Models. 2024. [arxiv]
- Du et al. Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model. 2024. [arxiv]
- Ma et al. Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation. ICML 2024. [arxiv]
- Liu et al. Dynamic Generation of Personalities with Large Language Models. 2024. [arxiv]
- Shang et al. How Far Have We Gone in Stripped Binary Code Understanding Using Large Language Models. 2024. [arxiv]
- Huang et al. LLMTune: Accelerate Database Knob Tuning with Large Language Models. 2024. [arxiv]
- Deng et al. Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction. 2024. [arxiv]
- Acikgoz et al. Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare. 2024. [arxiv]
- Zhang et al. Small Language Models Need Strong Verifiers to Self-Correct Reasoning. ACL 2024 Findings. [arxiv]
- Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. NAACL 2024. [arxiv]
- Xu et al. Large Language Models for Cyber Security: A Systematic Literature Review. 2024. [arxiv]
- Dammu et al. "They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversations. 2024. [arxiv]
- Yi et al. A safety realignment framework via subspace-oriented model fusion for large language models. 2024. [arxiv]
- Lou et al. SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling. 2024. [arxiv]
- Zhang et al. Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners. 2024. [arxiv]
- Zhang et al. TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models. 2024. [arxiv]
- Zihong Chen. Sentence Segmentation and Sentence Punctuation Based on XunziALLM. 2024. [paper]
- Gao et al. The Best of Both Worlds: Toward an Honest and Helpful Large Language Model. 2024. [arxiv]
- Wang and Song. MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset. 2024. [arxiv]
- Hu et al. Computational Limits of Low-Rank Adaptation (LoRA) for Transformer-Based Models. 2024. [arxiv]
- Ge et al. Time Sensitive Knowledge Editing through Efficient Finetuning. ACL 2024. [arxiv]
- Tan et al. Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions. 2024. [arxiv]
- Song et al. Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters. 2024. [arxiv]
- Gu et al. RWKV-CLIP: A Robust Vision-Language Representation Learner. 2024. [arxiv]
- Chen et al. Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees. 2024. [arxiv]
- Zhu et al. Are Large Language Models Good Statisticians?. 2024. [arxiv]
- Li et al. Know the Unknown: An Uncertainty-Sensitive Method for LLM Instruction Tuning. 2024. [arxiv]
- Ding et al. IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce. 2024. [arxiv]
- He et al. COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities. 2024. [arxiv]
- Lin et al. FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving. 2024. [arxiv]
- Treutlein et al. Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. 2024. [arxiv]
- Feng et al. SS-Bench: A Benchmark for Social Story Generation and Evaluation. 2024. [arxiv]
- Feng et al. Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement. 2024. [arxiv]
- Liu et al. Large Language Models for Cuffless Blood Pressure Measurement From Wearable Biosignals. 2024. [arxiv]
- Iyer et al. Exploring Very Low-Resource Translation with LLMs: The University of Edinburgh's Submission to AmericasNLP 2024 Translation Task. AmericasNLP 2024. [paper]
- Li et al. Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring. 2024. [arxiv]
- Yang et al. Financial Knowledge Large Language Model. 2024. [arxiv]
- Lin et al. DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging. 2024. [arxiv]
- Bako et al. Evaluating the Semantic Profiling Abilities of LLMs for Natural Language Utterances in Data Visualization. 2024. [arxiv]
- Huang et al. RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization. 2024. [arxiv]
- Jiang et al. LLM-Collaboration on Automatic Science Journalism for the General Audience. 2024. [arxiv]
- Inouye et al. Applied Auto-tuning on LoRA Hyperparameters. 2024. [paper]
- Qi et al. Research on Tibetan Tourism Viewpoints information generation system based on LLM. 2024. [arxiv]
- Xu et al. Course-Correction: Safety Alignment Using Synthetic Preferences. 2024. [arxiv]
- Sun et al. LAMBDA: A Large Model Based Data Agent. 2024. [arxiv]
- Zhu et al. CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in Healthcare. 2024. [arxiv]
- Yu et al. Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment. 2024. [arxiv]
- Xie et al. The Power of Personalized Datasets: Advancing Chinese Composition Writing for Elementary School through Targeted Model Fine-Tuning. IALP 2024. [paper]
- Liu et al. Instruct-Code-Llama: Improving Capabilities of Language Model in Competition Level Code Generation by Online Judge Feedback. ICIC 2024. [paper]
- Wang et al. Cybernetic Sentinels: Unveiling the Impact of Safety Data Selection on Model Security in Supervised Fine-Tuning. ICIC 2024. [paper]
- Xia et al. Understanding the Performance and Estimating the Cost of LLM Fine-Tuning. 2024. [arxiv]
- Zeng et al. Perceive, Reflect, and Plan: Designing LLM Agent for Goal-Directed City Navigation without Instructions. 2024. [arxiv]
- Xia et al. Using Pre-trained Language Model for Accurate ESG Prediction. FinNLP 2024. [paper]
- Liang et al. I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm. 2024. [arxiv]
- Bai et al. Aligning Large Language Model with Direct Multi-Preference Optimization for Recommendation. CIKM 2024. [paper]
- Zhang et al. CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling. ACL 2024. [paper]
- StarWhisper: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B.
- DISC-LawLLM: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge.
- Sunsimiao: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
- CareGPT: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B.
- MachineMindset: A series of MBTI Personality large language models, capable of giving any LLM 16 different personality types based on different datasets and training methods.
- Luminia-13B-v3: A large language model specialized in generate metadata for stable diffusion. [demo]
- Chinese-LLaVA-Med: A multimodal large language model specialized in Chinese medical domain, based on LLaVA-1.5-7B.
- AutoRE: A document-level relation extraction system based on large language models.
- NVIDIA RTX AI Toolkit: SDKs for fine-tuning LLMs on Windows PC for NVIDIA RTX.
- LazyLLM: An easy and lazy way for building multi-agent LLMs applications and supports model fine-tuning via LLaMA Factory.
- RAG-Retrieval: A full pipeline for RAG retrieval model fine-tuning, inference, and distillation. [blog]
- 360-LLaMA-Factory: A modified library that supports long sequence SFT & DPO using ring attention.
- Sky-T1: An o1-like model fine-tuned by NovaSky AI with very small cost.
- WeClone: One-stop solution for creating your digital avatar from chat logs.
- EmoLLM: A project about large language models (LLMs) and mental health.
License
This repository is licensed under the Apache-2.0 License.
Please follow the model licenses to use the corresponding model weights: BLOOM / DeepSeek / Falcon / Gemma / GLM-4 / GPT-2 / Granite / InternLM / Llama / Llama 2 / Llama 3 / Llama 4 / MiniCPM / Mistral/Mixtral/Pixtral / Phi-3/Phi-4 / Qwen / StarCoder 2 / TeleChat2 / Yuan 2
Citation
If this work is helpful, please kindly cite as:
@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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