base on An efficient, flexible and full-featured toolkit for fine-tuning LLM (InternLM2, Llama3, Phi3, Qwen, Mistral, ...) <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) 👋 join us on [](https://cdn.vansin.top/internlm/xtuner.jpg) [](https://twitter.com/intern_lm) [](https://discord.gg/xa29JuW87d) 🔍 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 | [简体中文](README_zh-CN.md) </div> ## 🚀 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> ## 🎉 News - **\[2024/07\]** Support [MiniCPM](xtuner/configs/minicpm/) models! - **\[2024/07\]** 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! - **\[2023/12\]** 🔥 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! - **\[2023/12\]** 🔥 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). ## 📖 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) 🚀, 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)). ## 🔥 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> 🔧</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> 🎨</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> ## 🛠️ 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) 🚀 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) 🚀. ```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 ``` 🔥 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. ## 🤝 Contributing We appreciate all contributions to XTuner. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline. ## 🎖️ 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) ## 🖊️ 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. ", Assign "at most 3 tags" to the expected json: {"id":"9342","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"