README_EN.md

Fine-Tuning Guide

• This project has undergone fine-tuning not only on mental health datasets but also on self-awareness, and here is the detailed guide for fine-tuning.

I. Fine-Tuning Based on Xtuner 🎉🎉🎉🎉🎉

Environment Setup

datasets==2.16.1
deepspeed==0.13.1
einops==0.7.0
flash_attn==2.5.0
mmengine==0.10.2
openxlab==0.0.34
peft==0.7.1
sentencepiece==0.1.99
torch==2.1.2
transformers==4.36.2
xtuner==0.1.11

You can also install them all at once by

cd xtuner_config/
pip3 install -r requirements.txt

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Fine-Tuning

cd xtuner_config/
xtuner train internlm2_7b_chat_qlora_e3.py --deepspeed deepspeed_zero2

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Convert the Obtained PTH Model to a HuggingFace Model

That is: Generate the Adapter folder

cd xtuner_config/
mkdir hf
export MKL_SERVICE_FORCE_INTEL=1

xtuner convert pth_to_hf internlm2_7b_chat_qlora_e3.py ./work_dirs/internlm_chat_7b_qlora_oasst1_e3_copy/epoch_3.pth ./hf

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Merge the HuggingFace Adapter with the Large Language Model

xtuner convert merge ./internlm2-chat-7b ./hf ./merged --max-shard-size 2GB
# xtuner convert merge \
#     ${NAME_OR_PATH_TO_LLM} \
#     ${NAME_OR_PATH_TO_ADAPTER} \
#     ${SAVE_PATH} \
#     --max-shard-size 2GB

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Testing

cd demo/
python cli_internlm2.py

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II. Fine-Tuning Based on Transformers🎉🎉🎉🎉🎉

• Please refer to the ChatGLM3-6b lora fine-tuning guide.

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Other

Feel free to give xtuner and EmoLLM a star~

🎉🎉🎉🎉🎉