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English | 简体中文

🚀 Speed Benchmark

  • Llama2 7B Training Speed
  • Llama2 70B Training Speed

🎉 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] 🔥 Support multi-modal VLM pretraining and fine-tuning with LLaVA-v1.5 architecture! Click here for details!
  • [2023/12] 🔥 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.

📖 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 🚀, 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).

🔥 Supports

Models SFT Datasets Data Pipelines Algorithms

🛠️ 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 🚀 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 🚀.

    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

    🔥 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.

🤝 Contributing

We appreciate all contributions to XTuner. Please refer to CONTRIBUTING.md for the contributing guideline.

🎖️ Acknowledgement

🖊️ 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.