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.