初始化项目，由ModelHub XC社区提供模型

Model: quicktensor/blockrank-msmarco-mistral-7b
Source: Original Platform

README.md

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