Skip to content

Latest commit

 

History

History
79 lines (54 loc) · 2.92 KB

README.md

File metadata and controls

79 lines (54 loc) · 2.92 KB
Raw

SFTLLMs_for_ChemText_Mining

Download

git clone https://github.com/zw-SIMM/SFTLLMs_for_chemtext_mining
cd SFTLLMs_for_ChemText_Mining

🖊 Datasets and Codes

Preprocessed data， fine-tuning codes， README workflows have been placed in corresponding folders:

  • Paragraph2Comound/

  • Paragraph2RXNRole/prod/ and Paragraph2RXNRole/role/

  • Paragraph2MOFInfo/

  • Paragraph2NMR/

  • Paragraph2Action/ (dataset is derived from pistachio dataset, which is available upon request.)

💿Fine-tuning ChatGPT (GPT-3.5-Turbo) and Prompt-Engineering GPT-4

Environment (OS: Windows or Linux)

pip install openai
pip install pandas

Note: The fine-tuning code has been slightly different as the version of openai updated to v1.0.0+.

Here, we provide the latest code.

Implementation

Specific scripts for each task are in the corresponding folders.

All notebooks of fine-tuning and prompt engineering GPTs (GPT-4, GPT-3.5) as well as evaluating for each task has beed released!

Demo of Fine-tuning ChatGPT on small dataset

Here, we gave an example notebook of fine-tuning ChatGPT on 25 Paragraph2NMR data in demo/fine-tuning_chatgpt_on_25_paragraph2NMR_data.ipynb, including:

  • Preprocessing
  • Training
  • Inferencing
  • Evaluating

📀Fine-tuning Open-source Language Models (Mistral, Llama3, Bart, T5)

Environment (Linux)

mamba create -n llm python=3.10
mamba activate llm 
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pandas numpy ipywidgets tqdm
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple torch==2.1.2  transformers==4.38.2 datasets tiktoken wandb==0.11 openpyxl
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple peft==0.8.0 accelerate bitsandbytes safetensors jsonlines
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple vllm==0.3.1
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple trl==0.7
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorboardX tensorboard
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple textdistance nltk matplotlib seaborn seqeval
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple modelscope

Pretrained Models Downloads

Open-sourced pretrained models (Llama3, Llama2, Mistral, Bart, T5) can be downloaded from huggingface or modelscope.

Here is an example for downloading pretrained models by scripts on linux servers from modelscope:

from modelscope import snapshot_download
model_dir = snapshot_download("LLM-Research/Meta-Llama-3-8B-Instruct", revision='master', cache_dir='/home/pretrained_models')
model_dir = snapshot_download('AI-ModelScope/Mistral-7B-Instruct-v0.2', revision='master', cache_dir='/home/pretrained_models')

Fine-tuning

The codes and tutorials of Fine-tuning Language models (ChatGPT, Llama3, Llama2, Mistral, Bart, T5) for each task are in the corresponding folders.