README.md

---
language: en
license: apache-2.0
---

# Shears Model Card: shears-mpt-7b-50-base

The sparsified [MPT-7B](https://huggingface.co/mosaicml/mpt-7b) with 50% sparsity as a base model in [Shears](https://arxiv.org/abs/2404.10934).

## Model Sources

**Repository:** [https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears](https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning/tree/main/Shears)

**Paper:** 
- [Shears: Unstructured Sparsity with Neural Low-rank Adapter Search](https://arxiv.org/abs/2404.10934)
- [Low-Rank Adapters Meet Neural Architecture Search for LLM Compression](https://arxiv.org/abs/2501.16372)

## Citation

```bash
@inproceedings{munoz-etal-2024-shears,
    title = "Shears: Unstructured Sparsity with Neural Low-rank Adapter Search",
    author = "Mu{\~n}oz, J. Pablo  and
      Yuan, Jinjie  and
      Jain, Nilesh",
    editor = "Yang, Yi  and
      Davani, Aida  and
      Sil, Avi  and
      Kumar, Anoop",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.naacl-industry.34",
    doi = "10.18653/v1/2024.naacl-industry.34",
    pages = "395--405",
}
```

## Acknowledgement

Thanks to the work Wanda ([paper](https://arxiv.org/abs/2306.11695), [code](https://github.com/locuslab/wanda)), which provides a simple but effective pruning approach.

## License

Apache-2.0