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* style: apply formatter * fix: add outdated warnings * docs: add dataset format and polish * docs: polish README * fix: fix json format * fix: fix typos * revert: revert 7b example |
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| README.md | ||
| easy_dataset.py | ||
| easy_models.py | ||
| train_peft_prompts.py | ||
| train_peft_sft.py | ||
README.md
⚠️ This content may be outdated since the major update of Colossal Chat. We will update this content soon.
Add Peft support for SFT and Prompts model training
The original implementation just adopts the loralib and merges the layers into the final model. The huggingface peft is a better lora model implementation and can be easily training and distributed.
Since reward model is relative small, I just keep it as original one. I suggest train full model to get the proper reward/critic model.
Preliminary installation
Since the current pypi peft package(0.2) has some bugs, please install the peft package using source.
git clone https://github.com/huggingface/peft
cd peft
pip install .
Usage
For SFT training, just call train_peft_sft.py
Its arguments are almost identical to train_sft.py instead adding a new eval_dataset if you have a eval_dataset file. The data file is just a plain datafile, please check the format in the easy_dataset.py.
For stage-3 rlhf training, call train_peft_prompts.py. Its arguments are almost identical to train_prompts.py. The only difference is that I use text files to indicate the prompt and pretrained data file. The models are included in easy_models.py. Currently only bloom models are tested, but technically gpt2/opt/llama should be supported.
Dataformat
Please refer the formats in test_sft.txt, test_prompts.txt, test_pretrained.txt.