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82 lines
3.8 KiB
Markdown
82 lines
3.8 KiB
Markdown
<h1 align="center">LLM SFT Example</h1>
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<p align="center">
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<img src="https://img.shields.io/badge/python-%E2%89%A53.8-5be.svg">
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<img src="https://img.shields.io/badge/pytorch-%E2%89%A51.12%20%7C%20%E2%89%A52.0-orange.svg">
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<a href="https://github.com/modelscope/modelscope/"><img src="https://img.shields.io/badge/modelscope-%E2%89%A51.8.1-5D91D4.svg"></a>
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<a href="https://github.com/modelscope/swift/"><img src="https://img.shields.io/badge/ms--swift-%E2%89%A51.0.0-6FEBB9.svg"></a>
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</p>
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<p align="center">
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<a href="https://modelscope.cn/home">Modelscope Hub</a>
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<br>
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<a href="README_CN.md">中文</a>  ｜  English
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</p>
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## Note
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1. This README.md file is **copied from** [ms-swift](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/README.md)
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2. This directory has been **migrated** to [ms-swift](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm), and the files in this directory are **no longer maintained**.
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## Features
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1. supported sft method: [lora](https://arxiv.org/abs/2106.09685), [qlora](https://arxiv.org/abs/2305.14314), full(full parameter fine tuning), ...
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2. supported models: [**qwen-7b**](https://github.com/QwenLM/Qwen-7B), baichuan-7b, baichuan-13b, chatglm2-6b, chatglm2-6b-32k, llama2-7b, llama2-13b, llama2-70b, openbuddy-llama2-13b, openbuddy-llama-65b, polylm-13b, ...
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3. supported feature: quantization, ddp, model parallelism(device map), gradient checkpoint, gradient accumulation steps, push to modelscope hub, custom datasets, ...
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4. supported datasets: alpaca-en(gpt4), alpaca-zh(gpt4), finance-en, multi-alpaca-all, code-en, instinwild-en, instinwild-zh, ...
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## Prepare the Environment
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Experimental environment: A10, 3090, A100, ... (V100 does not support bf16, quantization)
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```bash
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# Installing miniconda
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wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
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sh Miniconda3-latest-Linux-x86_64.sh
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# Setting up a conda virtual environment
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conda create --name ms-sft python=3.10
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conda activate ms-sft
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# Setting up a global pip mirror for faster downloads
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pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
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pip install torch torchvision torchaudio -U
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pip install sentencepiece charset_normalizer cpm_kernels tiktoken -U
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pip install matplotlib scikit-learn tqdm tensorboard -U
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pip install transformers datasets -U
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pip install accelerate transformers_stream_generator -U
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pip install ms-swift modelscope -U
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# Recommended installation from source code for faster bug fixes
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git clone https://github.com/modelscope/swift.git
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cd swift
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pip install -r requirements.txt
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pip install .
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# same as modelscope...(git clone ...)
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```
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## Run SFT and Inference
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```bash
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# Clone the repository and enter the code directory.
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git clone https://github.com/modelscope/swift.git
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cd swift/examples/pytorch/llm
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# sft(qlora) and infer qwen-7b, Requires 16GB VRAM.
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# If you want to use quantification, you need to `pip install bitsandbytes`
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bash scripts/qwen_7b/qlora/sft.sh
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# If you want to push the model to modelscope hub during training
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bash scripts/qwen_7b/qlora/sft_push_to_hub.sh
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bash scripts/qwen_7b/qlora/infer.sh
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# sft(qlora+ddp) and infer qwen-7b, Requires 4*16GB VRAM.
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bash scripts/qwen_7b/qlora_ddp/sft.sh
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bash scripts/qwen_7b/qlora_ddp/infer.sh
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# sft(full) and infer qwen-7b, Requires 95GB VRAM.
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bash scripts/qwen_7b/full/sft.sh
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bash scripts/qwen_7b/full/infer.sh
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# For more scripts, please see `scripts/` folder
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```
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## Extend Datasets
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1. If you need to extend the model, you can modify the `MODEL_MAPPING` in `utils/models.py`. `model_id` can be specified as a local path. In this case, `revision` doesn't work.
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2. If you need to extend or customize the dataset, you can modify the `DATASET_MAPPING` in `utils/datasets.py`. You need to customize the `get_*_dataset` function, which returns a dataset with two columns: `instruction`, `output`.
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