DaSLaM

This repository contains code for the paper Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning, accepted at EMNLP Main Conference 2023.

Authors: Gurusha Juneja, Subhabrata Dutta, Soumen Chakrabarti, Sunny Manchanda, Tanmoy Chakraborty

🛠 Dependencies and Installation

• torch 2.0.1+cu117
• sentencepiece 0.1.99
• transformers
• trl 0.4.1
• other dependencies in requirements.txt

# git clone this repository
git clone https://github.com/LCS2-IIITD/DaSLaM
cd DaSLaM

# install python dependencies
pip3 install -r requirements.txt

Supervised Finetune Stage

To fine-tune the model for question generation using SFT ru the script,

python3 QuesGenFinetune.py

RL Finetuning Stage

To further finetune the model using RLMF, make the following changes in file ppo_train_13B.py:

- line 216: Replace the folder name with the location of the 13B base llama model
- line 223: Replace the folder name with the location of the finetuned adapter
- line 250: Replace the folder name with the location of the 13B llama tokenizer 
- line 263: Replace the folder name with the location of the 13B base llama model
- line 264: Replace the folder name with the location of the 13B instruction finetuned llama adapter
- line 266: Replace the folder name with the location of the 13B llama tokenizer

Now run,

python3 LLAMA13B/Context/ppo_train_13B.py

📞 Contact

If you have any questions or issues, please feel free to reach out Gurusha Juneja at gurushajuneja@gmail.com.

✏️ Citation

If you think that this work is helpful, please feel free to leave a star ⭐️ and cite our paper:

@misc{juneja2023small,
      title={Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning}, 
      author={Gurusha Juneja and Subhabrata Dutta and Soumen Chakrabarti and Sunny Manchanda and Tanmoy Chakraborty},
      year={2023},
      eprint={2310.18338},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}