ChatGLM2-6B（windows本地微调实践）

# ChatGLM Efficient Tuning [](https://github.com/hiyouga/ChatGLM-Efficient-Tuning/stargazers) [](LICENSE) [](https://github.com/hiyouga/ChatGLM-Efficient-Tuning/commits/main) [](https://pypi.org/project/glmtuner/) [](https://github.com/hiyouga/ChatGLM-Efficient-Tuning/pulls) Fine-tuning 🤖[ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) model with 🤗[PEFT](https://github.com/huggingface/peft). 👋 Join our [WeChat](assets/wechat.jpg). \[ English | [中文](README_zh.md) \] If you have any questions, please refer to our [Wiki📄](https://github.com/hiyouga/ChatGLM-Efficient-Tuning/wiki). ## Notice This repo will **not be maintained** in the future. Please follow **[LLaMA-Efficient-Tuning](https://github.com/hiyouga/LLaMA-Efficient-Tuning)** for fine-tuning the language models (including ChatGLM2-6B). ## Changelog [23/07/15] Now we develop an all-in-one Web UI for training, evaluation and inference. Try `train_web.py` to fine-tune ChatGLM-6B model 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] Now we release [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. [23/06/25] Now we align 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. [23/06/25] Now we support fine-tuning the [ChatGLM2-6B](https://github.com/THUDM/ChatGLM2-6B) model with our framework! [23/06/05] Now we support 4-bit LoRA training (aka [QLoRA](https://github.com/artidoro/qlora)). Try `--quantization_bit 4` argument to work with 4-bit quantized model. (experimental feature) [23/06/01] We implemented a framework supporting the efficient tuning of LLaMA and BLOOM models. Please follow [LLaMA-Efficient-Tuning](https://github.com/hiyouga/LLaMA-Efficient-Tuning) if you are interested. [23/05/19] Now we support using the development set to evaluate the model while training. Try `--dev_ratio` argument to specify the size of development set. [23/04/29] Now we support training ChatGLM with **Reinforcement Learning with Human Feedback (RLHF)** ! We provide several examples to run RLHF training, please refer to the `examples` folder for details. [23/04/20] Our repo achieved 100 stars within 12 days! Congratulations! [23/04/19] Now we support **merging the weights** of fine-tuned models trained by LoRA! Try `--checkpoint_dir checkpoint1,checkpoint2` argument for continually fine-tuning the models. [23/04/18] Now we support training the **quantized models** using three fine-tuning methods! Try `quantization_bit` argument for training the model in 4/8 bits. [23/04/12] Now we support **training from checkpoints**! Use `--checkpoint_dir` argument to specify the checkpoint model to fine-tune from. [23/04/11] Now we support training with **combined datasets**! Try `--dataset dataset1,dataset2` argument for training with multiple datasets. ## Datasets - For supervised fine-tuning: - [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca) - [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca) - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1) - [Self-cognition (zh)](data/self_cognition.json) - [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection) - [RefGPT (zh)](https://github.com/sufengniu/RefGPT) - [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) - [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M) - [LIMA (en)](https://huggingface.co/datasets/GAIR/lima) - [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) - [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT) - [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa) - [UltraChat (en)](https://github.com/thunlp/UltraChat) - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn) - For reward modelling: - [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) 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 ``` ## Fine-Tuning Methods Our script now supports the following fine-tuning methods: - [LoRA](https://arxiv.org/abs/2106.09685) - Fine-tuning the low-rank adapters of the model. - [P-Tuning V2](https://github.com/THUDM/P-tuning-v2) - Fine-tuning the prefix encoder of the model. - [Freeze](https://arxiv.org/abs/2012.14913) - Fine-tuning the MLPs in the last n blocks of the model. - Full Tuning - Fine-tuning all the parameters of the model. ## Requirement - Python 3.8+ and PyTorch 1.13.1+ - 🤗Transformers, Datasets, Accelerate, PEFT and TRL - fire, protobuf, cpm-kernels and sentencepiece - jieba, rouge-chinese and nltk (used at evaluation) - gradio and matplotlib (used in train_web.py) - uvicorn, fastapi and sse-starlette (used in api_demo.py) And **powerful GPUs**! ## Getting Started ### Data Preparation (optional) Please refer to `data/example_dataset` 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 lfs install git clone https://github.com/hiyouga/ChatGLM-Efficient-Tuning.git conda create -n chatglm_etuning python=3.10 conda activate chatglm_etuning cd ChatGLM-Efficient-Tuning 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 12.1. ```bash pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl ``` ### All-in-one Web UI ```bash CUDA_VISIBLE_DEVICES=0 python src/train_web.py ``` Currently the web UI only supports training on **a single GPU**. ### Fine-tuning with a Single GPU ```bash CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \