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![# LLaMA Factory](assets/logo.png) | |
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👋 Join our [WeChat](assets/wechat.jpg). | |
\[ English | [ä¸æ–‡](README_zh.md) \] | |
**Fine-tuning a large language model can be easy as...** | |
https://github.com/hiyouga/LLaMA-Factory/assets/16256802/9840a653-7e9c-41c8-ae89-7ace5698baf6 | |
Choose your path: | |
- **🤗 Spaces**: https://huggingface.co/spaces/hiyouga/LLaMA-Board | |
- **ModelScope**: https://modelscope.cn/studios/hiyouga/LLaMA-Board | |
- **Colab**: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing | |
- **Local machine**: Please refer to [usage](#getting-started) | |
## Table of Contents | |
- [Features](#features) | |
- [Benchmark](#benchmark) | |
- [Changelog](#changelog) | |
- [Supported Models](#supported-models) | |
- [Supported Training Approaches](#supported-training-approaches) | |
- [Provided Datasets](#provided-datasets) | |
- [Requirement](#requirement) | |
- [Getting Started](#getting-started) | |
- [Projects using LLaMA Factory](#projects-using-llama-factory) | |
- [License](#license) | |
- [Citation](#citation) | |
- [Acknowledgement](#acknowledgement) | |
## Features | |
- **Various models**: LLaMA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc. | |
- **Integrated methods**: (Continuous) pre-training, supervised fine-tuning, reward modeling, PPO and DPO. | |
- **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. | |
## Benchmark | |
Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/ptuning), LLaMA-Factory's LoRA tuning offers up to **3.7 times faster** training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA-Factory's QLoRA further improves the efficiency regarding the GPU memory. | |
![benchmark](assets/benchmark.svg) | |
<details><summary>Definitions</summary> | |
- **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. | |
</details> | |
## Changelog | |
[24/03/13] We supported **[LoRA+](https://arxiv.org/abs/2402.12354)**. Try `loraplus_lr_ratio=16.0` to enable LoRA+ algorithm. | |
[24/03/07] We supported gradient low-rank projection (**[GaLore](https://arxiv.org/abs/2403.03507)**) algorithm. Try `--use_galore` to use the memory-efficient optimizer. | |
[24/03/07] We integrated **[vLLM](https://github.com/vllm-project/vllm)** for faster and concurrent inference. Try `--infer_backend vllm` to enjoy **270%** inference speed. (LoRA is not yet supported, merge it first.) | |
[24/02/28] We supported weight-decomposed LoRA (**[DoRA](https://arxiv.org/abs/2402.09353)**). Try `--use_dora` to activate DoRA training. | |
[24/02/15] We supported **block expansion** proposed by [LLaMA Pro](https://github.com/TencentARC/LLaMA-Pro). See `scripts/llama_pro.py` for usage. | |
<details><summary>Full Changelog</summary> | |
[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`. | |
[23/12/23] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s implementation to boost LoRA tuning for the LLaMA, Mistral and Yi models. Try `--use_unsloth` argument to activate unsloth patch. It achieves **170%** speed in our benchmark, check [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison) for details. | |
[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](#use-modelscope-hub-optional) for usage. | |
[23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `--neftune_noise_alpha` argument to activate NEFTune, e.g., `--neftune_noise_alpha 5`. | |
[23/09/27] We supported **$S^2$-Attn** proposed by [LongLoRA](https://github.com/dvlab-research/LongLoRA) for the LLaMA models. Try `--shift_attn` argument to enable shift short attention. | |
[23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [this example](#evaluation) to evaluate your models. | |
[23/09/10] We supported **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**. Try `--flash_attn` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs. | |
[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 [this example](#dpo-training) 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](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)**). Try `--quantization_bit 4/8` argument to work with quantized models. | |
</details> | |
## Supported Models | |
| Model | Model size | Default module | Template | | |
| -------------------------------------------------------- | --------------------------- | ----------------- | --------- | | |
| [Baichuan2](https://huggingface.co/baichuan-inc) | 7B/13B | W_pack | baichuan2 | | |
| [BLOOM](https://huggingface.co/bigscience/bloom) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - | | |
| [BLOOMZ](https://huggingface.co/bigscience/bloomz) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - | | |
| [ChatGLM3](https://huggingface.co/THUDM/chatglm3-6b) | 6B | query_key_value | chatglm3 | | |
| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B | q_proj,v_proj | deepseek | | |
| [Falcon](https://huggingface.co/tiiuae) | 7B/40B/180B | query_key_value | falcon | | |
| [Gemma](https://huggingface.co/google) | 2B/7B | q_proj,v_proj | gemma | | |
| [InternLM2](https://huggingface.co/internlm) | 7B/20B | wqkv | intern2 | | |
| [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | q_proj,v_proj | - | | |
| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 | | |
| [Mistral](https://huggingface.co/mistralai) | 7B | q_proj,v_proj | mistral | | |
| [Mixtral](https://huggingface.co/mistralai) | 8x7B | q_proj,v_proj | mistral | | |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | att_proj | olmo | | |
| [Phi-1.5/2](https://huggingface.co/microsoft) | 1.3B/2.7B | q_proj,v_proj | - | | |
| [Qwen](https://huggingface.co/Qwen) | 1.8B/7B/14B/72B | c_attn | qwen | | |
| [Qwen1.5](https://huggingface.co/Qwen) | 0.5B/1.8B/4B/7B/14B/72B | q_proj,v_proj | qwen | | |
| [StarCoder2](https://huggingface.co/bigcode) | 3B/7B/15B | q_proj,v_proj | - | | |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | q_proj,v_proj | xverse | | |
| [Yi](https://huggingface.co/01-ai) | 6B/9B/34B | q_proj,v_proj | yi | | |
| [Yuan](https://huggingface.co/IEITYuan) | 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](src/llmtuner/extras/constants.py) for a full list of models we supported. | |
You also can add a custom chat template to [template.py](src/llmtuner/data/template.py). | |
## Supported Training Approaches | |
| Approach | Full-tuning | Freeze-tuning | LoRA | QLoRA | | |
| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ | | |
| Pre-Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | | |
| Supervised Fine-Tuning | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | | |
| Reward Modeling | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | | |
| PPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | | |
| DPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | | |
> [!NOTE] | |
> Use `--quantization_bit 4` argument to enable QLoRA. | |
## Provided Datasets | |
<details><summary>Pre-training datasets</summary> | |
- [Wiki Demo (en)](data/wiki_demo.txt) | |
- [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | |
- [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2) | |
- [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220) | |
- [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered) | |
- [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile) | |
- [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B) | |
- [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack) | |
- [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata) | |
</details> | |
<details><summary>Supervised fine-tuning datasets</summary> | |
- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca) | |
- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca) | |
- [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) | |
- [Self Cognition (zh)](data/self_cognition.json) | |
- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1) | |
- [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection) | |
- [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) | |
- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN) | |
- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN) | |
- [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN) | |
- [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M) | |
- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M) | |
- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M) | |
- [UltraChat (en)](https://github.com/thunlp/UltraChat) | |
- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima) | |
- [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) | |
- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) | |
- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT) | |
- [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) | |
- [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen) | |
- [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k) | |
- [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4) | |
- [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) | |
- [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct) | |
- [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) | |
- [Glaive Function Calling V2 (en)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) | |
- [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) | |
- [Open Assistant (de)](https://huggingface.co/datasets/mayflowergmbh/oasst_de) | |
- [Dolly 15k (de)](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de) | |
- [Alpaca GPT4 (de)](https://huggingface.co/datasets/mayflowergmbh/alpaca-gpt4_de) | |
- [OpenSchnabeltier (de)](https://huggingface.co/datasets/mayflowergmbh/openschnabeltier_de) | |
- [Evol Instruct (de)](https://huggingface.co/datasets/mayflowergmbh/evol-instruct_de) | |
- [Dolphin (de)](https://huggingface.co/datasets/mayflowergmbh/dolphin_de) | |
- [Booksum (de)](https://huggingface.co/datasets/mayflowergmbh/booksum_de) | |
- [Airoboros (de)](https://huggingface.co/datasets/mayflowergmbh/airoboros-3.0_de) | |
- [Ultrachat (de)](https://huggingface.co/datasets/mayflowergmbh/ultra-chat_de) | |
</details> | |
<details><summary>Preference datasets</summary> | |
- [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf) | |
- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1) | |
- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) | |
- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar) | |
- [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de) | |
</details> | |
Please refer to [data/README.md](data/README.md) for details. | |
Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands. | |
```bash | |
pip install --upgrade huggingface_hub | |
huggingface-cli login | |
``` | |
## Requirement | |
| Mandatory | Minimum | Recommend | | |
| ------------ | ------- | --------- | | |
| python | 3.8 | 3.10 | | |
| torch | 1.13.1 | 2.2.0 | | |
| transformers | 4.37.2 | 4.38.2 | | |
| datasets | 2.14.3 | 2.17.1 | | |
| accelerate | 0.27.2 | 0.27.2 | | |
| peft | 0.9.0 | 0.9.0 | | |
| trl | 0.7.11 | 0.7.11 | | |
| Optional | Minimum | Recommend | | |
| ------------ | ------- | --------- | | |
| CUDA | 11.6 | 12.2 | | |
| deepspeed | 0.10.0 | 0.13.1 | | |
| bitsandbytes | 0.39.0 | 0.41.3 | | |
| flash-attn | 2.3.0 | 2.5.5 | | |
### Hardware Requirement | |
\* *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 | | |
## Getting Started | |
### Data Preparation (optional) | |
Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use a single `.json` file or a [dataset loading script](https://huggingface.co/docs/datasets/dataset_script) with multiple files to create a custom dataset. | |
> [!NOTE] | |
> Please update `data/dataset_info.json` to use your custom dataset. About the format of this file, please refer to `data/README.md`. | |
### Dependence Installation (optional) | |
```bash | |
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 -r requirements.txt | |
``` | |
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. | |
```bash | |
pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.40.0-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. | |
### Use ModelScope Hub (optional) | |
If you have trouble with downloading models and datasets from Hugging Face, you can use LLaMA-Factory together with ModelScope in the following manner. | |
```bash | |
export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows | |
``` | |
Then you can train the corresponding model by specifying a model ID of the ModelScope Hub. (find a full list of model IDs at [ModelScope Hub](https://modelscope.cn/models)) | |
```bash | |
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ | |
--model_name_or_path modelscope/Llama-2-7b-ms \ | |
... # arguments (same as below) | |
``` | |
LLaMA Board also supports using the models and datasets on the ModelScope Hub. | |
```bash | |
CUDA_VISIBLE_DEVICES=0 USE_MODELSCOPE_HUB=1 python src/train_web.py | |
``` | |
### Train on a single GPU | |
> [!IMPORTANT] | |
> If you want to train models on multiple GPUs, please refer to [Distributed Training](#distributed-training). | |
#### LLaMA Board GUI | |
```bash | |
CUDA_VISIBLE_DEVICES=0 python src/train_web.py | |
``` | |
#### Pre-Training | |
```bash | |
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ | |
--stage pt \ | |
--do_train \ | |
--model_name_or_path path_to_llama_model \ | |
--dataset wiki_demo \ | |
--finetuning_type lora \ | |
--lora_target q_proj,v_proj \ | |
--output_dir path_to_pt_checkpoint \ | |
--overwrite_cache \ | |
--per_device_train_batch_size 4 \ | |
--gradient_accumulation_steps 4 \ | |
--lr_scheduler_type cosine \ | |
--logging_steps 10 \ | |
--save_steps 1000 \ | |
--learning_rate 5e-5 \ | |
--num_train_epochs 3.0 \ | |
--plot_loss \ | |
--fp16 | |
``` | |
#### Supervised Fine-Tuning | |
```bash | |
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ | |
--stage sft \ | |
--do_train \ | |
--model_name_or_path path_to_llama_model \ | |
--dataset alpaca_gpt4_en \ | |
--template default \ | |
--finetuning_type lora \ | |
--lora_target q_proj,v_proj \ | |
--output_dir path_to_sft_checkpoint \ | |
--overwrite_cache \ | |
--per_device_train_batch_size 4 \ | |
--gradient_accumulation_steps 4 \ | |
--lr_scheduler_type cosine \ | |
--logging_steps 10 \ | |
--save_steps 1000 \ | |
--learning_rate 5e-5 \ | |
--num_train_epochs 3.0 \ | |
--plot_loss \ | |
--fp16 | |
``` | |
#### Reward Modeling | |
```bash | |
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ | |
--stage rm \ | |
--do_train \ | |
--model_name_or_path path_to_llama_model \ | |
--adapter_name_or_path path_to_sft_checkpoint \ | |
--create_new_adapter \ | |
--dataset comparison_gpt4_en \ | |
--template default \ | |
--finetuning_type lora \ | |
--lora_target q_proj,v_proj \ | |
--output_dir path_to_rm_checkpoint \ | |
--per_device_train_batch_size 2 \ | |
--gradient_accumulation_steps 4 \ | |
--lr_scheduler_type cosine \ | |
--logging_steps 10 \ | |
--save_steps 1000 \ | |
--learning_rate 1e-6 \ | |
--num_train_epochs 1.0 \ | |
--plot_loss \ | |
--fp16 | |
``` | |
#### PPO Training | |
```bash | |
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ | |
--stage ppo \ | |
--do_train \ | |
--model_name_or_path path_to_llama_model \ | |
--adapter_name_or_path path_to_sft_checkpoint \ | |
--create_new_adapter \ | |
--dataset alpaca_gpt4_en \ | |
--template default \ | |
--finetuning_type lora \ | |
--lora_target q_proj,v_proj \ | |
--reward_model path_to_rm_checkpoint \ | |
--output_dir path_to_ppo_checkpoint \ | |
--per_device_train_batch_size 2 \ | |
--gradient_accumulation_steps 4 \ | |
--lr_scheduler_type cosine \ | |
--top_k 0 \ | |
--top_p 0.9 \ | |
--logging_steps 10 \ | |
--save_steps 1000 \ | |
--learning_rate 1e-5 \ | |
--num_train_epochs 1.0 \ | |
--plot_loss \ | |
--fp16 | |
``` | |
> [!TIP] | |
> Use `--adapter_name_or_path path_to_sft_checkpoint,path_to_ppo_checkpoint` to infer the fine-tuned model. | |
> [!WARNING] | |
> Use `--per_device_train_batch_size=1` for LLaMA-2 models in fp16 PPO training. | |
#### DPO Training | |
```bash | |
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ | |
--stage dpo \ | |
--do_train \ | |
--model_name_or_path path_to_llama_model \ | |
--adapter_name_or_path path_to_sft_checkpoint \ | |
--create_new_adapter \ | |
--dataset comparison_gpt4_en \ | |
--template default \ | |
--finetuning_type lora \ | |
--lora_target q_proj,v_proj \ | |
--output_dir path_to_dpo_checkpoint \ | |
--per_device_train_batch_size 2 \ | |
--gradient_accumulation_steps 4 \ | |
--lr_scheduler_type cosine \ | |
--logging_steps 10 \ | |
--save_steps 1000 \ | |
--learning_rate 1e-5 \ | |
--num_train_epochs 1.0 \ | |
--plot_loss \ | |
--fp16 | |
``` | |
> [!TIP] | |
> Use `--adapter_name_or_path path_to_sft_checkpoint,path_to_dpo_checkpoint` to infer the fine-tuned model. | |
### Distributed Training | |
#### Use Huggingface Accelerate | |
```bash | |
accelerate launch --config_file config.yaml src/train_bash.py # arguments (same as above) | |
``` | |
<details><summary>Example config.yaml for LoRA training</summary> | |
```yaml | |
compute_environment: LOCAL_MACHINE | |
debug: false | |
distributed_type: MULTI_GPU | |
downcast_bf16: 'no' | |
gpu_ids: all | |
machine_rank: 0 | |
main_training_function: main | |
mixed_precision: fp16 | |
num_machines: 1 | |
num_processes: 4 | |
rdzv_backend: static | |
same_network: true | |
tpu_env: [] | |
tpu_use_cluster: false | |
tpu_use_sudo: false | |
use_cpu: false | |
``` | |
</details> | |
> [!TIP] | |
> We commend using Accelerate for LoRA tuning. | |
#### Use DeepSpeed | |
```bash | |
deepspeed --num_gpus 8 src/train_bash.py \ | |
--deepspeed ds_config.json \ | |
... # arguments (same as above) | |
``` | |
<details><summary>Example ds_config.json for full-parameter training with DeepSpeed ZeRO-2</summary> | |
```json | |
{ | |
"train_batch_size": "auto", | |
"train_micro_batch_size_per_gpu": "auto", | |
"gradient_accumulation_steps": "auto", | |
"gradient_clipping": "auto", | |
"zero_allow_untested_optimizer": true, | |
"fp16": { | |
"enabled": "auto", | |
"loss_scale": 0, | |
"loss_scale_window": 1000, | |
"initial_scale_power": 16, | |
"hysteresis": 2, | |
"min_loss_scale": 1 | |
}, | |
"bf16": { | |
"enabled": "auto" | |
}, | |
"zero_optimization": { | |
"stage": 2, | |
"allgather_partitions": true, | |
"allgather_bucket_size": 5e8, | |
"overlap_comm": true, | |
"reduce_scatter": true, | |
"reduce_bucket_size": 5e8, | |
"contiguous_gradients": true, | |
"round_robin_gradients": true | |
} | |
} | |
``` | |
</details> | |
> [!TIP] | |
> Refer to [examples](examples) for more training scripts. | |
### Merge LoRA weights and export model | |
```bash | |
CUDA_VISIBLE_DEVICES=0 python src/export_model.py \ | |
--model_name_or_path path_to_llama_model \ | |
--adapter_name_or_path path_to_checkpoint \ | |
--template default \ | |
--finetuning_type lora \ | |
--export_dir path_to_export \ | |
--export_size 2 \ | |
--export_legacy_format False | |
``` | |
> [!WARNING] | |
> Merging LoRA weights into a quantized model is not supported. | |
> [!TIP] | |
> Use `--model_name_or_path path_to_export` solely to use the exported model. | |
> | |
> Use `--export_quantization_bit 4` and `--export_quantization_dataset data/c4_demo.json` to quantize the model with AutoGPTQ after merging the LoRA weights. | |
### Inference with OpenAI-style API | |
```bash | |
CUDA_VISIBLE_DEVICES=0 API_PORT=8000 python src/api_demo.py \ | |
--model_name_or_path path_to_llama_model \ | |
--adapter_name_or_path path_to_checkpoint \ | |
--template default \ | |
--finetuning_type lora | |
``` | |
> [!TIP] | |
> Visit `http://localhost:8000/docs` for API documentation. | |
### Inference with command line | |
```bash | |
CUDA_VISIBLE_DEVICES=0 python src/cli_demo.py \ | |
--model_name_or_path path_to_llama_model \ | |
--adapter_name_or_path path_to_checkpoint \ | |
--template default \ | |
--finetuning_type lora | |
``` | |
### Inference with web browser | |
```bash | |
CUDA_VISIBLE_DEVICES=0 python src/web_demo.py \ | |
--model_name_or_path path_to_llama_model \ | |
--adapter_name_or_path path_to_checkpoint \ | |
--template default \ | |
--finetuning_type lora | |
``` | |
### Evaluation | |
```bash | |
CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \ | |
--model_name_or_path path_to_llama_model \ | |
--adapter_name_or_path path_to_checkpoint \ | |
--template vanilla \ | |
--finetuning_type lora \ | |
--task mmlu \ | |
--split test \ | |
--lang en \ | |
--n_shot 5 \ | |
--batch_size 4 | |
``` | |
### Predict | |
```bash | |
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ | |
--stage sft \ | |
--do_predict \ | |
--model_name_or_path path_to_llama_model \ | |
--adapter_name_or_path path_to_checkpoint \ | |
--dataset alpaca_gpt4_en \ | |
--template default \ | |
--finetuning_type lora \ | |
--output_dir path_to_predict_result \ | |
--per_device_eval_batch_size 1 \ | |
--max_samples 100 \ | |
--predict_with_generate \ | |
--fp16 | |
``` | |
> [!WARNING] | |
> Use `--per_device_train_batch_size=1` for LLaMA-2 models in fp16 predict. | |
> [!TIP] | |
> We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit predict. | |
### Dockerize Training | |
#### Get ready | |
Necessary dockerized environment is needed, such as Docker or Docker Compose. | |
#### Docker support | |
```bash | |
docker build -f ./Dockerfile -t llama-factory:latest . | |
docker run --gpus=all -v ./hf_cache:/root/.cache/huggingface/ -v ./data:/app/data -v ./output:/app/output -p 7860:7860 --shm-size 16G --name llama_factory -d llama-factory:latest | |
``` | |
#### Docker Compose support | |
```bash | |
docker compose -f ./docker-compose.yml up -d | |
``` | |
> [!TIP] | |
> Details about volume: | |
> * hf_cache: Utilize Huggingface cache on the host machine. Reassignable if a cache already exists in a different directory. | |
> * data: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI. | |
> * output: Set export dir to this location so that the merged result can be accessed directly on the host machine. | |
## Projects using LLaMA Factory | |
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. 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. 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. 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. 2024. [[arxiv]](https://arxiv.org/abs/2402.11809) | |
1. Cao et al. Head-wise Shareable Attention for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11819) | |
1. Zhang et al. Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages. 2024. [[arxiv]](https://arxiv.org/abs/2402.12204) | |
1. Kim et al. Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.14714) | |
1. **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B. | |
1. **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge. | |
1. **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B. | |
1. **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B. | |
1. **[MachineMindset](https://github.com/PKU-YuanGroup/Machine-Mindset/)**: A series of MBTI Personality large language models, capable of giving any LLM 16 different personality types based on different datasets and training methods. | |
> [!TIP] | |
> If you have a project that should be incorporated, please contact via email or create a pull request. | |
## License | |
This repository is licensed under the [Apache-2.0 License](LICENSE). | |
Please follow the model licenses to use the corresponding model weights: [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan) | |
## Citation | |
If this work is helpful, please kindly cite as: | |
```bibtex | |
@Misc{llama-factory, | |
title = {LLaMA Factory}, | |
author = {hiyouga}, | |
howpublished = {\url{https://github.com/hiyouga/LLaMA-Factory}}, | |
year = {2023} | |
} | |
``` | |
## Acknowledgement | |
This repo benefits from [PEFT](https://github.com/huggingface/peft), [QLoRA](https://github.com/artidoro/qlora) and [FastChat](https://github.com/lm-sys/FastChat). Thanks for their wonderful works. | |
## Star History | |
![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Factory&type=Date) | |