Awesome
<div align="center"> <img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/> <br /><br />English | įŽäŊä¸æ
</div>đ Speed Benchmark
- Llama2 7B Training Speed
- Llama2 70B Training Speed
đ News
- [2024/07] Support MiniCPM models!
- [2024/07] Support DPO, ORPO and Reward Model training with packed data and sequence parallel! See documents for more details.
- [2024/07] Support InternLM 2.5 models!
- [2024/06] Support DeepSeek V2 models! 2x faster!
- [2024/04] LLaVA-Phi-3-mini is released! Click here for details!
- [2024/04] LLaVA-Llama-3-8B and LLaVA-Llama-3-8B-v1.1 are released! Click here for details!
- [2024/04] Support Llama 3 models!
- [2024/04] Support Sequence Parallel for enabling highly efficient and scalable LLM training with extremely long sequence lengths! [Usage] [Speed Benchmark]
- [2024/02] Support Gemma models!
- [2024/02] Support Qwen1.5 models!
- [2024/01] Support InternLM2 models! The latest VLM LLaVA-Internlm2-7B / 20B models are released, with impressive performance!
- [2024/01] Support DeepSeek-MoE models! 20GB GPU memory is enough for QLoRA fine-tuning, and 4x80GB for full-parameter fine-tuning. Click here for details!
- [2023/12] đĨ Support multi-modal VLM pretraining and fine-tuning with LLaVA-v1.5 architecture! Click here for details!
- [2023/12] đĨ Support Mixtral 8x7B models! Click here for details!
- [2023/11] Support ChatGLM3-6B model!
- [2023/10] Support MSAgent-Bench dataset, and the fine-tuned LLMs can be applied by Lagent!
- [2023/10] Optimize the data processing to accommodate
system
context. More information can be found on Docs! - [2023/09] Support InternLM-20B models!
- [2023/09] Support Baichuan2 models!
- [2023/08] XTuner is released, with multiple fine-tuned adapters on Hugging Face.
đ Introduction
XTuner is an efficient, flexible and full-featured toolkit for fine-tuning large models.
Efficient
- Support LLM, VLM pre-training / fine-tuning on almost all GPUs. XTuner is capable of fine-tuning 7B LLM on a single 8GB GPU, as well as multi-node fine-tuning of models exceeding 70B.
- Automatically dispatch high-performance operators such as FlashAttention and Triton kernels to increase training throughput.
- Compatible with DeepSpeed đ, easily utilizing a variety of ZeRO optimization techniques.
Flexible
- Support various LLMs (InternLM, Mixtral-8x7B, Llama 2, ChatGLM, Qwen, Baichuan, ...).
- Support VLM (LLaVA). The performance of LLaVA-InternLM2-20B is outstanding.
- Well-designed data pipeline, accommodating datasets in any format, including but not limited to open-source and custom formats.
- Support various training algorithms (QLoRA, LoRA, full-parameter fune-tune), allowing users to choose the most suitable solution for their requirements.
Full-featured
- Support continuous pre-training, instruction fine-tuning, and agent fine-tuning.
- Support chatting with large models with pre-defined templates.
- The output models can seamlessly integrate with deployment and server toolkit (LMDeploy), and large-scale evaluation toolkit (OpenCompass, VLMEvalKit).
đĨ Supports
<table> <tbody> <tr align="center" valign="middle"> <td> <b>Models</b> </td> <td> <b>SFT Datasets</b> </td> <td> <b>Data Pipelines</b> </td> <td> <b>Algorithms</b> </td> </tr> <tr valign="top"> <td align="left" valign="top"> <ul> <li><a href="https://huggingface.co/internlm">InternLM2 / 2.5</a></li> <li><a href="https://huggingface.co/meta-llama">Llama 2 / 3</a></li> <li><a href="https://huggingface.co/collections/microsoft/phi-3-6626e15e9585a200d2d761e3">Phi-3</a></li> <li><a href="https://huggingface.co/THUDM/chatglm2-6b">ChatGLM2</a></li> <li><a href="https://huggingface.co/THUDM/chatglm3-6b">ChatGLM3</a></li> <li><a href="https://huggingface.co/Qwen/Qwen-7B">Qwen</a></li> <li><a href="https://huggingface.co/baichuan-inc/Baichuan2-7B-Base">Baichuan2</a></li> <li><a href="https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1">Mixtral</a></li> <li><a href="https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat">DeepSeek V2</a></li> <li><a href="https://huggingface.co/google">Gemma</a></li> <li><a href="https://huggingface.co/openbmb">MiniCPM</a></li> <li>...</li> </ul> </td> <td> <ul> <li><a href="https://modelscope.cn/datasets/damo/MSAgent-Bench">MSAgent-Bench</a></li> <li><a href="https://huggingface.co/datasets/fnlp/moss-003-sft-data">MOSS-003-SFT</a> đ§</li> <li><a href="https://huggingface.co/datasets/tatsu-lab/alpaca">Alpaca en</a> / <a href="https://huggingface.co/datasets/silk-road/alpaca-data-gpt4-chinese">zh</a></li> <li><a href="https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k">WizardLM</a></li> <li><a href="https://huggingface.co/datasets/timdettmers/openassistant-guanaco">oasst1</a></li> <li><a href="https://huggingface.co/datasets/garage-bAInd/Open-Platypus">Open-Platypus</a></li> <li><a href="https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K">Code Alpaca</a></li> <li><a href="https://huggingface.co/datasets/burkelibbey/colors">Colorist</a> đ¨</li> <li><a href="https://github.com/WangRongsheng/ChatGenTitle">Arxiv GenTitle</a></li> <li><a href="https://github.com/LiuHC0428/LAW-GPT">Chinese Law</a></li> <li><a href="https://huggingface.co/datasets/Open-Orca/OpenOrca">OpenOrca</a></li> <li><a href="https://huggingface.co/datasets/shibing624/medical">Medical Dialogue</a></li> <li>...</li> </ul> </td> <td> <ul> <li><a href="docs/zh_cn/user_guides/incremental_pretraining.md">Incremental Pre-training</a> </li> <li><a href="docs/zh_cn/user_guides/single_turn_conversation.md">Single-turn Conversation SFT</a> </li> <li><a href="docs/zh_cn/user_guides/multi_turn_conversation.md">Multi-turn Conversation SFT</a> </li> </ul> </td> <td> <ul> <li><a href="http://arxiv.org/abs/2305.14314">QLoRA</a></li> <li><a href="http://arxiv.org/abs/2106.09685">LoRA</a></li> <li>Full parameter fine-tune</li> <li><a href="https://arxiv.org/abs/2305.18290">DPO</a></li> <li><a href="https://arxiv.org/abs/2403.07691">ORPO</a></li> <li>Reward Model</a></li> </ul> </td> </tr> </tbody> </table>đ ī¸ Quick Start
Installation
-
It is recommended to build a Python-3.10 virtual environment using conda
conda create --name xtuner-env python=3.10 -y conda activate xtuner-env
-
Install XTuner via pip
pip install -U xtuner
or with DeepSpeed integration
pip install -U 'xtuner[deepspeed]'
-
Install XTuner from source
git clone https://github.com/InternLM/xtuner.git cd xtuner pip install -e '.[all]'
Fine-tune
XTuner supports the efficient fine-tune (e.g., QLoRA) for LLMs. Dataset prepare guides can be found on dataset_prepare.md.
-
Step 0, prepare the config. XTuner provides many ready-to-use configs and we can view all configs by
xtuner list-cfg
Or, if the provided configs cannot meet the requirements, please copy the provided config to the specified directory and make specific modifications by
xtuner copy-cfg ${CONFIG_NAME} ${SAVE_PATH} vi ${SAVE_PATH}/${CONFIG_NAME}_copy.py
-
Step 1, start fine-tuning.
xtuner train ${CONFIG_NAME_OR_PATH}
For example, we can start the QLoRA fine-tuning of InternLM2.5-Chat-7B with oasst1 dataset by
# On a single GPU xtuner train internlm2_5_chat_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2 # On multiple GPUs (DIST) NPROC_PER_NODE=${GPU_NUM} xtuner train internlm2_5_chat_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2 (SLURM) srun ${SRUN_ARGS} xtuner train internlm2_5_chat_7b_qlora_oasst1_e3 --launcher slurm --deepspeed deepspeed_zero2
-
--deepspeed
means using DeepSpeed đ to optimize the training. XTuner comes with several integrated strategies including ZeRO-1, ZeRO-2, and ZeRO-3. If you wish to disable this feature, simply remove this argument. -
For more examples, please see finetune.md.
-
-
Step 2, convert the saved PTH model (if using DeepSpeed, it will be a directory) to Hugging Face model, by
xtuner convert pth_to_hf ${CONFIG_NAME_OR_PATH} ${PTH} ${SAVE_PATH}
Chat
XTuner provides tools to chat with pretrained / fine-tuned LLMs.
xtuner chat ${NAME_OR_PATH_TO_LLM} --adapter {NAME_OR_PATH_TO_ADAPTER} [optional arguments]
For example, we can start the chat with InternLM2.5-Chat-7B :
xtuner chat internlm/internlm2_5-chat-7b --prompt-template internlm2_chat
For more examples, please see chat.md.
Deployment
-
Step 0, merge the Hugging Face adapter to pretrained LLM, by
xtuner convert merge \ ${NAME_OR_PATH_TO_LLM} \ ${NAME_OR_PATH_TO_ADAPTER} \ ${SAVE_PATH} \ --max-shard-size 2GB
-
Step 1, deploy fine-tuned LLM with any other framework, such as LMDeploy đ.
pip install lmdeploy python -m lmdeploy.pytorch.chat ${NAME_OR_PATH_TO_LLM} \ --max_new_tokens 256 \ --temperture 0.8 \ --top_p 0.95 \ --seed 0
đĨ Seeking efficient inference with less GPU memory? Try 4-bit quantization from LMDeploy! For more details, see here.
Evaluation
- We recommend using OpenCompass, a comprehensive and systematic LLM evaluation library, which currently supports 50+ datasets with about 300,000 questions.
đ¤ Contributing
We appreciate all contributions to XTuner. Please refer to CONTRIBUTING.md for the contributing guideline.
đī¸ Acknowledgement
đī¸ Citation
@misc{2023xtuner,
title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
author={XTuner Contributors},
howpublished = {\url{https://github.com/InternLM/xtuner}},
year={2023}
}
License
This project is released under the Apache License 2.0. Please also adhere to the Licenses of models and datasets being used.