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Fine-tuning a large language model can be easy as...
tutorial_en.mp4
Choose your path:
- Colab: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
- Local machine: Please refer to usage
- Features
- Benchmark
- Changelog
- Supported Models
- Supported Training Approaches
- Provided Datasets
- Requirement
- Getting Started
- Projects using LLaMA Factory
- License
- Citation
- Acknowledgement
- Various models: LLaMA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
- Integrated methods: (Continuous) pre-training, supervised fine-tuning, reward modeling, PPO, DPO and ORPO.
- Scalable resources: 32-bit full-tuning, 16-bit freeze-tuning, 16-bit LoRA and 2/4/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8.
- Advanced algorithms: GaLore, DoRA, LongLoRA, LLaMA Pro, LoRA+, LoftQ and Agent tuning.
- Practical tricks: FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA.
- Experiment monitors: LlamaBoard, TensorBoard, Wandb, MLflow, etc.
- Faster inference: OpenAI-style API, Gradio UI and CLI with vLLM worker.
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 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=128for ChatGLM's P-Tuning andlora_rank=32for LLaMA Factory's LoRA tuning.
[24/03/31] We supported ORPO. See examples/lora_single_gpu 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/extras/fsdp_qlora for usage.
Full Changelog
[24/03/13] We supported LoRA+. See examples/extras/loraplus for usage.
[24/03/07] We supported gradient low-rank projection (GaLore) algorithm. See examples/extras/galore for usage.
[24/03/07] We integrated vLLM for faster and concurrent inference. Try --infer_backend vllm to enjoy 270% inference speed. (LoRA is not yet supported, merge it first.)
[24/02/28] We supported weight-decomposed LoRA (DoRA). Try --use_dora to activate DoRA training.
[24/02/15] We supported block expansion proposed by LLaMA Pro. See examples/extras/llama_pro for usage.
[24/02/05] Qwen1.5 (Qwen2 beta version) series models are supported in LLaMA-Factory. Check this blog post for details.
[24/01/18] We supported agent tuning for most models, equipping model with tool using abilities by fine-tuning with --dataset glaive_toolcall.
[23/12/23] We supported unsloth's implementation to boost LoRA tuning for the LLaMA, Mistral and Yi models. Try --use_unsloth argument to activate unsloth patch. It achieves 170% speed in our benchmark, check this page 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 for Chinese mainland users. See this tutorial for usage.
[23/10/21] We supported NEFTune trick for fine-tuning. Try --neftune_noise_alpha argument to activate NEFTune, e.g., --neftune_noise_alpha 5.
[23/09/27] We supported --shift_attn argument to enable shift short attention.
[23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See this example to evaluate your models.
[23/09/10] We supported FlashAttention-2. Try --flash_attn 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 this example to train your models.
[23/07/31] We supported dataset streaming. Try --streaming and --max_steps 10000 arguments to load your dataset in streaming mode.
[23/07/29] We released two instruction-tuned 13B models at Hugging Face. See these Hugging Face Repos (LLaMA-2 / 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). Try --quantization_bit 4/8 argument to work with quantized models.
| Model | Model size | Default module | Template |
|---|---|---|---|
| Baichuan2 | 7B/13B | W_pack | baichuan2 |
| BLOOM | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
| BLOOMZ | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
| ChatGLM3 | 6B | query_key_value | chatglm3 |
| DeepSeek (MoE) | 7B/16B/67B | q_proj,v_proj | deepseek |
| Falcon | 7B/40B/180B | query_key_value | falcon |
| Gemma | 2B/7B | q_proj,v_proj | gemma |
| InternLM2 | 7B/20B | wqkv | intern2 |
| LLaMA | 7B/13B/33B/65B | q_proj,v_proj | - |
| LLaMA-2 | 7B/13B/70B | q_proj,v_proj | llama2 |
| Mistral/Mixtral | 7B/8x7B | q_proj,v_proj | mistral |
| OLMo | 1B/7B | att_proj | olmo |
| Phi-1.5/2 | 1.3B/2.7B | q_proj,v_proj | - |
| Qwen | 1.8B/7B/14B/72B | c_attn | qwen |
| Qwen1.5 (MoE) | 0.5B/1.8B/4B/7B/14B/32B/72B | q_proj,v_proj | qwen |
| StarCoder2 | 3B/7B/15B | q_proj,v_proj | - |
| XVERSE | 7B/13B/65B | q_proj,v_proj | xverse |
| Yi | 6B/9B/34B | q_proj,v_proj | yi |
| Yuan | 2B/51B/102B | q_proj,v_proj | yuan |
Note
Default module is used for the --lora_target argument, you can use --lora_target all to specify all the available modules.
For the "base" models, the --template argument can be chosen from default, alpaca, vicuna etc. But make sure to use the corresponding template for the "chat" models.
Please refer to constants.py for a full list of models we supported.
You also can add a custom chat template to template.py.
| Approach | Full-tuning | Freeze-tuning | LoRA | QLoRA |
|---|---|---|---|---|
| Pre-Training | ✅ | ✅ | ✅ | ✅ |
| Supervised Fine-Tuning | ✅ | ✅ | ✅ | ✅ |
| Reward Modeling | ✅ | ✅ | ✅ | ✅ |
| PPO Training | ✅ | ✅ | ✅ | ✅ |
| DPO Training | ✅ | ✅ | ✅ | ✅ |
| ORPO Training | ✅ | ✅ | ✅ | ✅ |
Pre-training datasets
Supervised fine-tuning datasets
- Stanford Alpaca (en)
- Stanford Alpaca (zh)
- Alpaca GPT4 (en&zh)
- Self Cognition (zh)
- Open Assistant (multilingual)
- ShareGPT (zh)
- 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)
- LIMA (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)
- Ad Gen (zh)
- ShareGPT Hyperfiltered (en)
- ShareGPT4 (en&zh)
- UltraChat 200k (en)
- AgentInstruct (en)
- LMSYS Chat 1M (en)
- Evol Instruct V2 (en)
- Glaive Function Calling V2 (en)
- Cosmopedia (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
Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands.
pip install --upgrade huggingface_hub
huggingface-cli login| Mandatory | Minimum | Recommend |
|---|---|---|
| python | 3.8 | 3.10 |
| torch | 1.13.1 | 2.2.0 |
| transformers | 4.37.2 | 4.39.3 |
| datasets | 2.14.3 | 2.18.0 |
| accelerate | 0.27.2 | 0.28.0 |
| peft | 0.9.0 | 0.10.0 |
| trl | 0.8.1 | 0.8.1 |
| Optional | Minimum | Recommend |
|---|---|---|
| CUDA | 11.6 | 12.2 |
| deepspeed | 0.10.0 | 0.14.0 |
| bitsandbytes | 0.39.0 | 0.43.0 |
| flash-attn | 2.3.0 | 2.5.6 |
* estimated
| Method | Bits | 7B | 13B | 30B | 70B | 8x7B |
|---|---|---|---|---|---|---|
| Full | AMP | 120GB | 240GB | 600GB | 1200GB | 900GB |
| Full | 16 | 60GB | 120GB | 300GB | 600GB | 400GB |
| GaLore | 16 | 16GB | 32GB | 64GB | 160GB | 120GB |
| Freeze | 16 | 20GB | 40GB | 80GB | 200GB | 160GB |
| LoRA | 16 | 16GB | 32GB | 64GB | 160GB | 120GB |
| QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | 60GB |
| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 30GB |
| QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | 18GB |
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.
git clone https://github.com/hiyouga/LLaMA-Factory.git
conda create -n llama_factory python=3.10
conda activate llama_factory
cd LLaMA-Factory
pip install -e .[metrics]Extra dependencies available: deepspeed, metrics, unsloth, galore, vllm, bitsandbytes, gptq, awq, aqlm, qwen, modelscope, quality
For Windows users
If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you will be required to install a pre-built version of bitsandbytes library, which supports CUDA 11.1 to 12.2, please select the appropriate release version based on your CUDA version.
pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whlTo 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 based on your requirements.
Important
LLaMA Board GUI only supports training on a single GPU, please use CLI for distributed training.
export CUDA_VISIBLE_DEVICES=0 # `set CUDA_VISIBLE_DEVICES=0` for Windows
python src/train_web.py # or python -m llmtuner.webui.interfacedocker build -f ./Dockerfile -t llama-factory:latest .
docker run --gpus=all \
-v ./hf_cache:/root/.cache/huggingface/ \
-v ./data:/app/data \
-v ./output:/app/output \
-e CUDA_VISIBLE_DEVICES=0 \
-p 7860:7860 \
--shm-size 16G \
--name llama_factory \
-d llama-factory:latestdocker compose -f ./docker-compose.yml up -dDetails about volume
- hf_cache: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory.
- data: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
- output: Set export dir to this location so that the merged result can be accessed directly on the host machine.
See examples/README.md for usage.
Use python src/train_bash.py -h to display arguments description.
CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 python src/api_demo.py \
--model_name_or_path mistralai/Mistral-7B-Instruct-v0.2 \
--template mistral \
--infer_backend vllm \
--vllm_enforce_eagerIf 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 WindowsTrain 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., modelscope/Llama-2-7b-ms.
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. 2024. [arxiv]
- Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. 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 Collaps. 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. 2024. [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. 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]
- 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. 2024. [arxiv]
- 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.
This repository is licensed under the Apache-2.0 License.
Please follow the model licenses to use the corresponding model weights: Baichuan2 / BLOOM / ChatGLM3 / DeepSeek / Falcon / Gemma / InternLM2 / LLaMA / LLaMA-2 / Mistral / OLMo / Phi-1.5/2 / Qwen / StarCoder2 / XVERSE / Yi / Yuan
If this work is helpful, please kindly cite as:
@article{zheng2024llamafactory,
title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},
author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Yongqiang Ma},
journal={arXiv preprint arXiv:2403.13372},
year={2024},
url={http://arxiv.org/abs/2403.13372}
}This repo benefits from PEFT, TRL, QLoRA and FastChat. Thanks for their wonderful works.