<div align="center">
<img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/>
<br /><br />
[](https://github.com/InternLM/xtuner/stargazers)
[](https://github.com/InternLM/xtuner/blob/main/LICENSE)
[](https://pypi.org/project/xtuner/)
[](https://pypi.org/project/xtuner/)
[](https://github.com/InternLM/xtuner/issues)
[](https://github.com/InternLM/xtuner/issues)
đ join us on [](https://cdn.vansin.top/internlm/xtuner.jpg)
[](https://twitter.com/intern_lm)
[](https://discord.gg/xa29JuW87d)
đ Explore our models on
[](https://huggingface.co/xtuner)
[](https://www.modelscope.cn/organization/xtuner)
[](https://openxlab.org.cn/usercenter/xtuner)
[](https://www.wisemodel.cn/organization/xtuner)
English | [įŽäŊä¸æ](README_zh-CN.md)
</div>
## đ Speed Benchmark
- Llama2 7B Training Speed
<div align=center>
<img src="https://github.com/InternLM/xtuner/assets/41630003/9c9dfdf4-1efb-4daf-84bf-7c379ae40b8b" style="width:80%">
</div>
- Llama2 70B Training Speed
<div align=center>
<img src="https://github.com/InternLM/xtuner/assets/41630003/5ba973b8-8885-4b72-b51b-c69fa1583bdd" style="width:80%">
</div>
## đ News
- **\[2024/07\]** Support [DPO](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/dpo), [ORPO](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/orpo) and [Reward Model](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/reward_model) training with packed data and sequence parallel! See [documents](https://xtuner.readthedocs.io/en/latest/dpo/overview.html) for more details.
- **\[2024/07\]** Support [InternLM 2.5](xtuner/configs/internlm/internlm2_5_chat_7b/) models!
- **\[2024/06\]** Support [DeepSeek V2](xtuner/configs/deepseek/deepseek_v2_chat/) models! **2x faster!**
- **\[2024/04\]** [LLaVA-Phi-3-mini](https://huggingface.co/xtuner/llava-phi-3-mini-hf) is released! Click [here](xtuner/configs/llava/phi3_mini_4k_instruct_clip_vit_large_p14_336) for details!
- **\[2024/04\]** [LLaVA-Llama-3-8B](https://huggingface.co/xtuner/llava-llama-3-8b) and [LLaVA-Llama-3-8B-v1.1](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1) are released! Click [here](xtuner/configs/llava/llama3_8b_instruct_clip_vit_large_p14_336) for details!
- **\[2024/04\]** Support [Llama 3](xtuner/configs/llama) models!
- **\[2024/04\]** Support Sequence Parallel for enabling highly efficient and scalable LLM training with extremely long sequence lengths! \[[Usage](https://github.com/InternLM/xtuner/blob/docs/docs/zh_cn/acceleration/train_extreme_long_sequence.rst)\] \[[Speed Benchmark](https://github.com/InternLM/xtuner/blob/docs/docs/zh_cn/acceleration/benchmark.rst)\]
- **\[2024/02\]** Support [Gemma](xtuner/configs/gemma) models!
- **\[2024/02\]** Support [Qwen1.5](xtuner/configs/qwen/qwen1_5) models!
- **\[2024/01\]** Support [InternLM2](xtuner/configs/internlm) models! The latest VLM [LLaVA-Internlm2-7B](https://huggingface.co/xtuner/llava-internlm2-7b) / [20B](https://huggingface.co/xtuner/llava-internlm2-20b) models are released, with impressive performance!
- **\[2024/01\]** Support [DeepSeek-MoE](https://huggingface.co/deepseek-ai/deepseek-moe-16b-chat) models! 20GB GPU memory is enough for QLoRA fine-tuning, and 4x80GB for full-parameter fine-tuning. Click [here](xtuner/configs/deepseek/) for details!
- **\[2023/12\]** đĨ Support multi-modal VLM pretraining and fine-tuning with [LLaVA-v1.5](https://github.com/haotian-liu/LLaVA) architecture! Click [here](xtuner/configs/llava/README.md) for details!
- **\[2023/12\]** đĨ Support [Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) models! Click [here](xtuner/configs/mixtral/README.md) for details!
- **\[2023/11\]** Support [ChatGLM3-6B](xtuner/configs/chatglm) model!
- **\[2023/10\]** Support [MSAgent-Bench](https://modelscope.cn/datasets/damo/MSAgent-Bench) dataset, and the fine-tuned LLMs can be applied by [Lagent](https://github.com/InternLM/lagent)!
- **\[2023/10\]** Optimize the data processing to accommodate `system` context. More information can be found on [Docs](docs/en/user_guides/dataset_format.md)!
- **\[2023/09\]** Support [InternLM-20B](xtuner/configs/internlm) models!
- **\[2023/09\]** Support [Baichuan2](xtuner/configs/baichuan) models!
- **\[2023/08\]** XTuner is released, with multiple fine-tuned adapters on [Hugging Face](https://huggingface.co/xtuner).
## 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](https://github.com/microsoft/DeepSpeed) đ, easily utilizing a variety of ZeRO optimization techniques.
**Flexible**
- Support various LLMs ([InternLM](https://huggingface.co/internlm), [Mixtral-8x7B](https://huggingface.co/mistralai), [Llama 2](https://huggingface.co/meta-llama), [ChatGLM](https://huggingface.co/THUDM), [Qwen](https://huggingface.co/Qwen), [Baichuan](https://huggingface.co/baichuan-inc), ...).
- Support VLM ([LLaVA](https://github.com/haotian-liu/LLaVA)). The performance of [LLaVA-InternLM2-20B](https://huggingface.co/xtuner/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](http://arxiv.org/abs/2305.14314), [LoRA](http://arxiv.org/abs/2106.09685), 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](https://github.com/InternLM/lmdeploy)), and large-scale evaluation toolkit ([OpenCompass](https://github.com/open-compass/opencompass), [VLMEvalKit](https://github.com/open-compass/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>...</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
```bash
conda create --name xtuner-env python=3.10 -y
conda activate xtuner-env
```
- Install XTuner via pip
```shell
pip install -U xtuner
```
or with DeepSpeed integration
```shell
pip install -U 'xtuner[deepspeed]'
```
- Install XTuner from source
```shell
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](./docs/en/user_guides/dataset_prepare.md).
- **Step 0**, prepare the config. XTuner provides many ready-to-use configs and we can view all configs by
```shell
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
```shell
xtuner copy-cfg ${CONFIG_NAME} ${SAVE_PATH}
vi ${SAVE_PATH}/${CONFIG_NAME}_copy.py
```
- **Step 1**, start fine-tuning.
```shell
xtuner train ${CONFIG_NAME_OR_PATH}
```
For example, we can start the QLoRA fine-tuning of InternLM2.5-Chat-7B with oasst1 dataset by
```shell
# On a single GPU
xtuner train internlm2_5_chat_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2
# On multiple GPUs
(DIST) NPROC_PER_NODE=${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](https://github.com/microsoft/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](./docs/en/user_guides/finetune.md).
- **Step 2**, convert the saved PTH model (if using DeepSpeed, it will be a directory) to Hugging Face model, by
```shell
xtuner convert pth_to_hf ${CONFIG_NAME_OR_PATH} ${PTH} ${SAVE_PATH}
```
### Chat
XTuner provides tools to chat with pretrained / fine-tuned LLMs.
```shell
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 :
```shell
xtuner chat internlm/internlm2_5-chat-7b --prompt-template internlm2_chat
```
For more examples, please see [chat.md](./docs/en/user_guides/chat.md).
### Deployment
- **Step 0**, merge the Hugging Face adapter to pretrained LLM, by
```shell
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](https://github.com/InternLM/lmdeploy) đ.
```shell
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](https://github.com/InternLM/lmdeploy)! For more details, see [here](https://github.com/InternLM/lmdeploy/tree/main#quantization).
### Evaluation
- We recommend using [OpenCompass](https://github.com/InternLM/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](.github/CONTRIBUTING.md) for the contributing guideline.
## đī¸ Acknowledgement
- [Llama 2](https://github.com/facebookresearch/llama)
- [DeepSpeed](https://github.com/microsoft/DeepSpeed)
- [QLoRA](https://github.com/artidoro/qlora)
- [LMDeploy](https://github.com/InternLM/lmdeploy)
- [LLaVA](https://github.com/haotian-liu/LLaVA)
## đī¸ Citation
```bibtex
@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](LICENSE). Please also adhere to the Licenses of models and datasets being used.
Raw data
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"home_page": "https://github.com/InternLM/xtuner",
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"maintainer": null,
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"requires_python": "<3.11,>=3.8",
"maintainer_email": null,
"keywords": "large language model, parameter-efficient fine-tuning",
"author": "XTuner Contributors",
"author_email": "openmmlab@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/a0/56/5766fd0f630fbf3c8dc059321888028bf77927878f0c558ab946cab9487f/xtuner-0.1.23.tar.gz",
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"description": "<div align=\"center\">\n <img src=\"https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8\" width=\"600\"/>\n <br /><br />\n\n[](https://github.com/InternLM/xtuner/stargazers)\n[](https://github.com/InternLM/xtuner/blob/main/LICENSE)\n[](https://pypi.org/project/xtuner/)\n[](https://pypi.org/project/xtuner/)\n[](https://github.com/InternLM/xtuner/issues)\n[](https://github.com/InternLM/xtuner/issues)\n\n\ud83d\udc4b join us on [](https://cdn.vansin.top/internlm/xtuner.jpg)\n[](https://twitter.com/intern_lm)\n[](https://discord.gg/xa29JuW87d)\n\n\ud83d\udd0d Explore our models on\n[](https://huggingface.co/xtuner)\n[](https://www.modelscope.cn/organization/xtuner)\n[](https://openxlab.org.cn/usercenter/xtuner)\n[](https://www.wisemodel.cn/organization/xtuner)\n\nEnglish | [\u7b80\u4f53\u4e2d\u6587](README_zh-CN.md)\n\n</div>\n\n## \ud83d\ude80 Speed Benchmark\n\n- Llama2 7B Training Speed\n\n<div align=center>\n <img src=\"https://github.com/InternLM/xtuner/assets/41630003/9c9dfdf4-1efb-4daf-84bf-7c379ae40b8b\" style=\"width:80%\">\n</div>\n\n- Llama2 70B Training Speed\n\n<div align=center>\n <img src=\"https://github.com/InternLM/xtuner/assets/41630003/5ba973b8-8885-4b72-b51b-c69fa1583bdd\" style=\"width:80%\">\n</div>\n\n## \ud83c\udf89 News\n\n- **\\[2024/07\\]** Support [DPO](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/dpo), [ORPO](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/orpo) and [Reward Model](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/reward_model) training with packed data and sequence parallel! See [documents](https://xtuner.readthedocs.io/en/latest/dpo/overview.html) for more details.\n- **\\[2024/07\\]** Support [InternLM 2.5](xtuner/configs/internlm/internlm2_5_chat_7b/) models!\n- **\\[2024/06\\]** Support [DeepSeek V2](xtuner/configs/deepseek/deepseek_v2_chat/) models! **2x faster!**\n- **\\[2024/04\\]** [LLaVA-Phi-3-mini](https://huggingface.co/xtuner/llava-phi-3-mini-hf) is released! Click [here](xtuner/configs/llava/phi3_mini_4k_instruct_clip_vit_large_p14_336) for details!\n- **\\[2024/04\\]** [LLaVA-Llama-3-8B](https://huggingface.co/xtuner/llava-llama-3-8b) and [LLaVA-Llama-3-8B-v1.1](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1) are released! Click [here](xtuner/configs/llava/llama3_8b_instruct_clip_vit_large_p14_336) for details!\n- **\\[2024/04\\]** Support [Llama 3](xtuner/configs/llama) models!\n- **\\[2024/04\\]** Support Sequence Parallel for enabling highly efficient and scalable LLM training with extremely long sequence lengths! \\[[Usage](https://github.com/InternLM/xtuner/blob/docs/docs/zh_cn/acceleration/train_extreme_long_sequence.rst)\\] \\[[Speed Benchmark](https://github.com/InternLM/xtuner/blob/docs/docs/zh_cn/acceleration/benchmark.rst)\\]\n- **\\[2024/02\\]** Support [Gemma](xtuner/configs/gemma) models!\n- **\\[2024/02\\]** Support [Qwen1.5](xtuner/configs/qwen/qwen1_5) models!\n- **\\[2024/01\\]** Support [InternLM2](xtuner/configs/internlm) models! The latest VLM [LLaVA-Internlm2-7B](https://huggingface.co/xtuner/llava-internlm2-7b) / [20B](https://huggingface.co/xtuner/llava-internlm2-20b) models are released, with impressive performance!\n- **\\[2024/01\\]** Support [DeepSeek-MoE](https://huggingface.co/deepseek-ai/deepseek-moe-16b-chat) models! 20GB GPU memory is enough for QLoRA fine-tuning, and 4x80GB for full-parameter fine-tuning. Click [here](xtuner/configs/deepseek/) for details!\n- **\\[2023/12\\]** \ud83d\udd25 Support multi-modal VLM pretraining and fine-tuning with [LLaVA-v1.5](https://github.com/haotian-liu/LLaVA) architecture! Click [here](xtuner/configs/llava/README.md) for details!\n- **\\[2023/12\\]** \ud83d\udd25 Support [Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) models! Click [here](xtuner/configs/mixtral/README.md) for details!\n- **\\[2023/11\\]** Support [ChatGLM3-6B](xtuner/configs/chatglm) model!\n- **\\[2023/10\\]** Support [MSAgent-Bench](https://modelscope.cn/datasets/damo/MSAgent-Bench) dataset, and the fine-tuned LLMs can be applied by [Lagent](https://github.com/InternLM/lagent)!\n- **\\[2023/10\\]** Optimize the data processing to accommodate `system` context. More information can be found on [Docs](docs/en/user_guides/dataset_format.md)!\n- **\\[2023/09\\]** Support [InternLM-20B](xtuner/configs/internlm) models!\n- **\\[2023/09\\]** Support [Baichuan2](xtuner/configs/baichuan) models!\n- **\\[2023/08\\]** XTuner is released, with multiple fine-tuned adapters on [Hugging Face](https://huggingface.co/xtuner).\n\n## \ud83d\udcd6 Introduction\n\nXTuner is an efficient, flexible and full-featured toolkit for fine-tuning large models.\n\n**Efficient**\n\n- 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.\n- Automatically dispatch high-performance operators such as FlashAttention and Triton kernels to increase training throughput.\n- Compatible with [DeepSpeed](https://github.com/microsoft/DeepSpeed) \ud83d\ude80, easily utilizing a variety of ZeRO optimization techniques.\n\n**Flexible**\n\n- Support various LLMs ([InternLM](https://huggingface.co/internlm), [Mixtral-8x7B](https://huggingface.co/mistralai), [Llama 2](https://huggingface.co/meta-llama), [ChatGLM](https://huggingface.co/THUDM), [Qwen](https://huggingface.co/Qwen), [Baichuan](https://huggingface.co/baichuan-inc), ...).\n- Support VLM ([LLaVA](https://github.com/haotian-liu/LLaVA)). The performance of [LLaVA-InternLM2-20B](https://huggingface.co/xtuner/llava-internlm2-20b) is outstanding.\n- Well-designed data pipeline, accommodating datasets in any format, including but not limited to open-source and custom formats.\n- Support various training algorithms ([QLoRA](http://arxiv.org/abs/2305.14314), [LoRA](http://arxiv.org/abs/2106.09685), full-parameter fune-tune), allowing users to choose the most suitable solution for their requirements.\n\n**Full-featured**\n\n- Support continuous pre-training, instruction fine-tuning, and agent fine-tuning.\n- Support chatting with large models with pre-defined templates.\n- The output models can seamlessly integrate with deployment and server toolkit ([LMDeploy](https://github.com/InternLM/lmdeploy)), and large-scale evaluation toolkit ([OpenCompass](https://github.com/open-compass/opencompass), [VLMEvalKit](https://github.com/open-compass/VLMEvalKit)).\n\n## \ud83d\udd25 Supports\n\n<table>\n<tbody>\n<tr align=\"center\" valign=\"middle\">\n<td>\n <b>Models</b>\n</td>\n<td>\n <b>SFT Datasets</b>\n</td>\n<td>\n <b>Data Pipelines</b>\n</td>\n <td>\n <b>Algorithms</b>\n</td>\n</tr>\n<tr valign=\"top\">\n<td align=\"left\" valign=\"top\">\n<ul>\n <li><a href=\"https://huggingface.co/internlm\">InternLM2 / 2.5</a></li>\n <li><a href=\"https://huggingface.co/meta-llama\">Llama 2 / 3</a></li>\n <li><a href=\"https://huggingface.co/collections/microsoft/phi-3-6626e15e9585a200d2d761e3\">Phi-3</a></li>\n <li><a href=\"https://huggingface.co/THUDM/chatglm2-6b\">ChatGLM2</a></li>\n <li><a href=\"https://huggingface.co/THUDM/chatglm3-6b\">ChatGLM3</a></li>\n <li><a href=\"https://huggingface.co/Qwen/Qwen-7B\">Qwen</a></li>\n <li><a href=\"https://huggingface.co/baichuan-inc/Baichuan2-7B-Base\">Baichuan2</a></li>\n <li><a href=\"https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1\">Mixtral</a></li>\n <li><a href=\"https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat\">DeepSeek V2</a></li>\n <li><a href=\"https://huggingface.co/google\">Gemma</a></li>\n <li>...</li>\n</ul>\n</td>\n<td>\n<ul>\n <li><a href=\"https://modelscope.cn/datasets/damo/MSAgent-Bench\">MSAgent-Bench</a></li>\n <li><a href=\"https://huggingface.co/datasets/fnlp/moss-003-sft-data\">MOSS-003-SFT</a> \ud83d\udd27</li>\n <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>\n <li><a href=\"https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k\">WizardLM</a></li>\n <li><a href=\"https://huggingface.co/datasets/timdettmers/openassistant-guanaco\">oasst1</a></li>\n <li><a href=\"https://huggingface.co/datasets/garage-bAInd/Open-Platypus\">Open-Platypus</a></li>\n <li><a href=\"https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K\">Code Alpaca</a></li>\n <li><a href=\"https://huggingface.co/datasets/burkelibbey/colors\">Colorist</a> \ud83c\udfa8</li>\n <li><a href=\"https://github.com/WangRongsheng/ChatGenTitle\">Arxiv GenTitle</a></li>\n <li><a href=\"https://github.com/LiuHC0428/LAW-GPT\">Chinese Law</a></li>\n <li><a href=\"https://huggingface.co/datasets/Open-Orca/OpenOrca\">OpenOrca</a></li>\n <li><a href=\"https://huggingface.co/datasets/shibing624/medical\">Medical Dialogue</a></li>\n <li>...</li>\n</ul>\n</td>\n<td>\n<ul>\n <li><a href=\"docs/zh_cn/user_guides/incremental_pretraining.md\">Incremental Pre-training</a> </li>\n <li><a href=\"docs/zh_cn/user_guides/single_turn_conversation.md\">Single-turn Conversation SFT</a> </li>\n <li><a href=\"docs/zh_cn/user_guides/multi_turn_conversation.md\">Multi-turn Conversation SFT</a> </li>\n</ul>\n</td>\n<td>\n<ul>\n <li><a href=\"http://arxiv.org/abs/2305.14314\">QLoRA</a></li>\n <li><a href=\"http://arxiv.org/abs/2106.09685\">LoRA</a></li>\n <li>Full parameter fine-tune</li>\n <li><a href=\"https://arxiv.org/abs/2305.18290\">DPO</a></li>\n <li><a href=\"https://arxiv.org/abs/2403.07691\">ORPO</a></li>\n <li>Reward Model</a></li>\n</ul>\n</td>\n</tr>\n</tbody>\n</table>\n\n## \ud83d\udee0\ufe0f Quick Start\n\n### Installation\n\n- It is recommended to build a Python-3.10 virtual environment using conda\n\n ```bash\n conda create --name xtuner-env python=3.10 -y\n conda activate xtuner-env\n ```\n\n- Install XTuner via pip\n\n ```shell\n pip install -U xtuner\n ```\n\n or with DeepSpeed integration\n\n ```shell\n pip install -U 'xtuner[deepspeed]'\n ```\n\n- Install XTuner from source\n\n ```shell\n git clone https://github.com/InternLM/xtuner.git\n cd xtuner\n pip install -e '.[all]'\n ```\n\n### Fine-tune\n\nXTuner supports the efficient fine-tune (*e.g.*, QLoRA) for LLMs. Dataset prepare guides can be found on [dataset_prepare.md](./docs/en/user_guides/dataset_prepare.md).\n\n- **Step 0**, prepare the config. XTuner provides many ready-to-use configs and we can view all configs by\n\n ```shell\n xtuner list-cfg\n ```\n\n Or, if the provided configs cannot meet the requirements, please copy the provided config to the specified directory and make specific modifications by\n\n ```shell\n xtuner copy-cfg ${CONFIG_NAME} ${SAVE_PATH}\n vi ${SAVE_PATH}/${CONFIG_NAME}_copy.py\n ```\n\n- **Step 1**, start fine-tuning.\n\n ```shell\n xtuner train ${CONFIG_NAME_OR_PATH}\n ```\n\n For example, we can start the QLoRA fine-tuning of InternLM2.5-Chat-7B with oasst1 dataset by\n\n ```shell\n # On a single GPU\n xtuner train internlm2_5_chat_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2\n # On multiple GPUs\n (DIST) NPROC_PER_NODE=${GPU_NUM} xtuner train internlm2_5_chat_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2\n (SLURM) srun ${SRUN_ARGS} xtuner train internlm2_5_chat_7b_qlora_oasst1_e3 --launcher slurm --deepspeed deepspeed_zero2\n ```\n\n - `--deepspeed` means using [DeepSpeed](https://github.com/microsoft/DeepSpeed) \ud83d\ude80 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.\n\n - For more examples, please see [finetune.md](./docs/en/user_guides/finetune.md).\n\n- **Step 2**, convert the saved PTH model (if using DeepSpeed, it will be a directory) to Hugging Face model, by\n\n ```shell\n xtuner convert pth_to_hf ${CONFIG_NAME_OR_PATH} ${PTH} ${SAVE_PATH}\n ```\n\n### Chat\n\nXTuner provides tools to chat with pretrained / fine-tuned LLMs.\n\n```shell\nxtuner chat ${NAME_OR_PATH_TO_LLM} --adapter {NAME_OR_PATH_TO_ADAPTER} [optional arguments]\n```\n\nFor example, we can start the chat with InternLM2.5-Chat-7B :\n\n```shell\nxtuner chat internlm/internlm2_5-chat-7b --prompt-template internlm2_chat\n```\n\nFor more examples, please see [chat.md](./docs/en/user_guides/chat.md).\n\n### Deployment\n\n- **Step 0**, merge the Hugging Face adapter to pretrained LLM, by\n\n ```shell\n xtuner convert merge \\\n ${NAME_OR_PATH_TO_LLM} \\\n ${NAME_OR_PATH_TO_ADAPTER} \\\n ${SAVE_PATH} \\\n --max-shard-size 2GB\n ```\n\n- **Step 1**, deploy fine-tuned LLM with any other framework, such as [LMDeploy](https://github.com/InternLM/lmdeploy) \ud83d\ude80.\n\n ```shell\n pip install lmdeploy\n python -m lmdeploy.pytorch.chat ${NAME_OR_PATH_TO_LLM} \\\n --max_new_tokens 256 \\\n --temperture 0.8 \\\n --top_p 0.95 \\\n --seed 0\n ```\n\n \ud83d\udd25 Seeking efficient inference with less GPU memory? Try 4-bit quantization from [LMDeploy](https://github.com/InternLM/lmdeploy)! For more details, see [here](https://github.com/InternLM/lmdeploy/tree/main#quantization).\n\n### Evaluation\n\n- We recommend using [OpenCompass](https://github.com/InternLM/opencompass), a comprehensive and systematic LLM evaluation library, which currently supports 50+ datasets with about 300,000 questions.\n\n## \ud83e\udd1d Contributing\n\nWe appreciate all contributions to XTuner. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.\n\n## \ud83c\udf96\ufe0f Acknowledgement\n\n- [Llama 2](https://github.com/facebookresearch/llama)\n- [DeepSpeed](https://github.com/microsoft/DeepSpeed)\n- [QLoRA](https://github.com/artidoro/qlora)\n- [LMDeploy](https://github.com/InternLM/lmdeploy)\n- [LLaVA](https://github.com/haotian-liu/LLaVA)\n\n## \ud83d\udd8a\ufe0f Citation\n\n```bibtex\n@misc{2023xtuner,\n title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},\n author={XTuner Contributors},\n howpublished = {\\url{https://github.com/InternLM/xtuner}},\n year={2023}\n}\n```\n\n## License\n\nThis project is released under the [Apache License 2.0](LICENSE). Please also adhere to the Licenses of models and datasets being used.\n\n\n",
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