This repository hosts the code and data for our paper, Suri: Multi-constraint instruction following for long-form text generation.

We release 🦙 Suri, a single-turn instruction following dataset with multi-constraint instructions and long-form gold responses (2k-5k words). We also introduce I-ORPO, a variant of Odds Ratio Preference Optimization (ORPO) that accepts (x+, x-, y) as inputs rather than (x, y+, y-). We demonstrate the effectiveness of the dataset by fine-tuning Mistral-7B-Instruct with the SFT and I-ORPO method.

• [2024-06-25]: The code and data for Suri are now available.

1. Install the requirements for Suri:

   conda create -n suri python=3.10 
   conda activate suri
   pip install -r requirements.txt
   python -m pip install flash-attn --no-build-isolation
   huggingface-cli login       # Log in to Huggingface using your access token 
   sudo apt-get install git-lfs
   

2. Set up Huggingface cache directory:
   • Open your shell configuration file, which is typically ~/.bashrc or ~/.bash_profile for Bash, or ~/.zshrc for Zsh.
   • Add HF_HOME huggingface cache directory path to your configuration file: HF_HOME=/path/to/huggingface_cache.
   • Add HF_TOKEN huggingface access token to your configuration file: HF_TOKEN=<your_token>.
   • Save and close the file. Source the file to apply the changes: source ~/.bashrc or source ~/.bash_profile or source ~/.zshrc.
   • Double-check that the environment variable is set correctly: echo $HF_HOME.

.
├── README.md
├── assets
│   ├── img
│   └── styles
├── data
├── eval
│   ├── automatic
│   ├── human
│   └── inference
├── ft
│   ├── README.md
│   ├── deepspeed_zero3.yaml
│   ├── i-orpo
│   ├── lib
│   │   ├── alignment_mod
│   │   └── trl_mod
│   └── sft
├── index.html
├── prompts
├── requirements.txt
└── utils.py

• data contains b3.py, which can be used to reconstruct the gold responses of the books3 subset.
• eval contains:
  • automatic, which includes code to compute the ranking accuracy metric.
  • human, which includes the XML code for the human evaluation interfaces.
  • inference, which includes code to do inference with the fine-tuned models using either Transformers Huggingface or vLLM.
• ft contains code to fine-tune the models using I-ORPO or SFT:
  • i-orpo directory includes orpo.yaml, which defines the training hyperparameters; run_orpo.py, which contains the training code; and run_orpo.sh, which consolidates the training process into a single executable command.
  • sft directory includes sft.yaml, which defines the training hyperparameters; run_sft.py, which contains the training code; and run_sft.sh, which consolidates the training process into a single executable command.
  • deepspeed_zero3.yaml contains the hyperparameters for deepspeed zero3.
• prompts contains all prompts used in the paper.

• The dataset is available on Huggingface: https://huggingface.co/datasets/chtmp223/suri/.
• Due to copyright concerns, we do not release the gold responses that are sampled from the Books3 subset. For users with local access to the Books3 dataset, we include a script (data/b3.py) to reconstruct this portion of the dataset.
  • First, make sure to set the DATA_DIR variable to the path of the books3 dataset on your local machine.
  • Next, modify the code to either save the reconstructed dataset to a csv file or push to a new Huggingface repository.
  • Finally, run the code using python b3.py.

• Suri-I-ORPO is available on Huggingface: https://huggingface.co/chtmp223/suri-i-orpo. Suri-SFT is also available on Huggingface: https://huggingface.co/chtmp223/suri-sft.
• We include the code for training in the ft/ directory. See the README.md file in that folder for more information.
• We recommend inference with Huggingface Transformers library. See the model card and eval/ folder for more details on inference.

@misc{pham2024surimulticonstraintinstructionfollowing,
      title={Suri: Multi-constraint Instruction Following for Long-form Text Generation}, 
      author={Chau Minh Pham and Simeng Sun and Mohit Iyyer},
      year={2024},
      eprint={2406.19371},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2406.19371}, 
}