Add model card for SEPO
#1
by
nielsr
HF Staff
- opened
README.md
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---
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pipeline_tag: text-generation
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---
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# Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods
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This repository contains the code for the `SEPO` algorithm presented in the paper: [Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods](https://huggingface.co/papers/2502.01384).
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`SEPO` (Score Entropy Policy Optimization) is an efficient, broadly applicable, and theoretically justified policy gradient algorithm for fine-tuning discrete diffusion models over non-differentiable rewards. Our numerical experiments across several discrete generative tasks demonstrate the scalability and efficiency of our method, including applications on fine-tuning a masked diffusion language model on DNA sequences.
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<p align="center">
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<img src="https://github.com/ozekri/SEPO/blob/main/img/denoising_RLHF.gif" width=80% height=80% alt="Denoising RLHF process visualization">
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</p>
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For more details and the full implementation, please refer to the [official GitHub repository](https://github.com/ozekri/SEPO).
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## Sample Usage: Download Checkpoint
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You can download the fine-tuned models from Hugging Face directly using the `huggingface_hub` Python library to reproduce results:
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```python
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from huggingface_hub import hf_hub_download
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# Example: Download the SEPO fine-tuned model checkpoint
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ckpt_path = hf_hub_download(
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repo_id="Xssama/SEPO_DNA",
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filename="finetuned_sepo_kl.ckpt", # finetuned_sepo_kl_gf.ckpt for SEPO with gradient flow
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cache_dir="./checkpoints" # Optional: specify your preferred local directory
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)
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print(f"Checkpoint downloaded to: {ckpt_path}")
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```
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Alternatively, you can use `wget`:
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```bash
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wget https://huggingface.co/Xssama/SEPO-DNA/resolve/main/finetuned_sepo_kl.ckpt -P ./checkpoints/
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```
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## Citation
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If you find this work useful in your research, please consider citing:
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```bibtex
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@article{zekri2025fine,
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title={Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods},
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author={Zekri, Oussama and Boull{\'e}, Nicolas},
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journal={arXiv preprint arXiv:2502.01384},
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year={2025}
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}
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```
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