88 lines
1.6 KiB
Markdown
88 lines
1.6 KiB
Markdown
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# Fine-Tuning Guide
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- 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.
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## I. Fine-Tuning Based on Xtuner 🎉🎉🎉🎉🎉
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### Environment Setup
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```markdown
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datasets==2.16.1
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deepspeed==0.13.1
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einops==0.7.0
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flash_attn==2.5.0
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mmengine==0.10.2
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openxlab==0.0.34
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peft==0.7.1
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sentencepiece==0.1.99
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torch==2.1.2
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transformers==4.36.2
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xtuner==0.1.11
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```
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You can also install them all at once by
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```bash
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cd xtuner_config/
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pip3 install -r requirements.txt
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```
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---
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### Fine-Tuning
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```bash
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cd xtuner_config/
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xtuner train internlm2_7b_chat_qlora_e3.py --deepspeed deepspeed_zero2
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```
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---
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### Convert the Obtained PTH Model to a HuggingFace Model
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**That is: Generate the Adapter folder**
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```bash
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cd xtuner_config/
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mkdir hf
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export MKL_SERVICE_FORCE_INTEL=1
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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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```
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---
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### Merge the HuggingFace Adapter with the Large Language Model
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```bash
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xtuner convert merge ./internlm2-chat-7b ./hf ./merged --max-shard-size 2GB
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# xtuner convert merge \
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# ${NAME_OR_PATH_TO_LLM} \
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# ${NAME_OR_PATH_TO_ADAPTER} \
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# ${SAVE_PATH} \
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# --max-shard-size 2GB
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```
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---
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### Testing
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```
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cd demo/
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python cli_internlm2.py
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```
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---
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## II. Fine-Tuning Based on Transformers🎉🎉🎉🎉🎉
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- Please refer to the [ChatGLM3-6b lora fine-tuning guide](ChatGLM3-6b-ft.md).
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---
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## Other
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Feel free to give [xtuner](https://github.com/InternLM/xtuner) and [EmoLLM](https://github.com/aJupyter/EmoLLM) a star~
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🎉🎉🎉🎉🎉
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