64 lines
2.4 KiB
Markdown
64 lines
2.4 KiB
Markdown
---
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license: mit
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language:
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- en
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library_name: transformers
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tags:
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- text-generation
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- information-retrieval
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- ranking
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- reranking
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- blockrank
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- mistral
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base_model: mistralai/Mistral-7B-Instruct-v0.3
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datasets:
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- quicktensor/blockrank-msmarco-train-10p
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metrics:
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- ndcg
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- mrr
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---
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# BlockRank-Mistral-7B: Scalable In-context Ranking with Generative Models
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[](https://colab.research.google.com/github/nilesh2797/BlockRank/blob/main/quickstart.ipynb)
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**BlockRank-Mistral-7B** is a fine-tuned version of [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) optimized for efficient in-context document ranking. It implements BlockRank, a method that makes LLMs efficient and scalable for ranking by aligning their internal attention mechanisms with the structure of the ranking task.
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<p align="center">
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<img src="https://raw.githubusercontent.com/nilesh2797/BlockRank/main/assets/blockrank_diagram.png" alt="BlockRank Architecture" width="600"/>
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</p>
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### Key Features
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- **Linear Complexity Attention**: Structured sparse attention reduces complexity from O(n²) to O(n)
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- **2-4× Faster Inference**: Attention-based scoring eliminates autoregressive decoding
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- **Auxiliary Contrastive Loss**: Mid-layer contrastive objective improves relevance signals
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- **Strong Zero-shot Generalization**: SOTA performance on BEIR benchmarks
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## Citation
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If you use this model, please cite:
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```bibtex
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@article{gupta2025blockrank,
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title={Scalable In-context Ranking with Generative Models},
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author={Gupta, Nilesh and You, Chong and Bhojanapalli, Srinadh and Kumar, Sanjiv and Dhillon, Inderjit and Yu, Felix},
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journal={arXiv preprint arXiv:2510.05396},
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year={2025}
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}
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```
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## Model Card Contact
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For questions or issues, please open an issue on [GitHub](https://github.com/nilesh2797/BlockRank/issues).
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## Additional Resources
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- **Paper**: [arXiv:2510.05396](https://arxiv.org/abs/2510.05396)
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- **Code**: [GitHub Repository](https://github.com/nilesh2797/BlockRank)
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- **Dataset**: [HuggingFace Dataset](https://huggingface.co/datasets/nilesh2797/icr-msmarco-10p-train)
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- **Demo**: [Colab Notebook](https://colab.research.google.com/github/nilesh2797/BlockRank/blob/main/examples/quickstart.ipynb)
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## License
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This model is released under the MIT License. See [LICENSE](https://github.com/nilesh2797/BlockRank/blob/main/LICENSE) for details. |