hltcoe/Rank-K-32B
hltcoe/Rank-K-32B is a 32.8 billion parameter model developed by hltcoe, specifically designed for test-time reasoning in listwise reranking tasks. This model focuses on improving the ordering of lists, leveraging its large parameter count and a notable 131,072 token context length to enhance reranking performance. It is primarily optimized for applications requiring precise list ordering and relevance judgments.
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Rank-K: Test-Time Reasoning for Listwise Reranking
hltcoe/Rank-K-32B is a 32.8 billion parameter model developed by hltcoe, engineered for advanced listwise reranking through test-time reasoning. This model is distinguished by its focus on optimizing the order of lists, a critical task in information retrieval and recommendation systems. It leverages a substantial 131,072 token context length, allowing it to process extensive input sequences for more nuanced reranking decisions.
Key Capabilities
- Listwise Reranking: Specialized in reordering lists of items to improve relevance or quality.
- Test-Time Reasoning: Incorporates reasoning capabilities during inference to enhance reranking accuracy.
- Large Context Window: Utilizes a 131,072 token context length, enabling the processing of long documents or extensive item lists for comprehensive analysis.
Good For
- Information Retrieval: Improving the ranking of search results.
- Recommendation Systems: Enhancing the order of recommended items.
- Any application requiring precise list ordering: Where the sequence of items is crucial for performance.
For more technical details and to run the model, refer to the official GitHub repository and the associated research paper.