rank1 7b: Test Time Compute for Reranking in Information Retrieval 📄 Paper 🚀 GitHub Repository rank1 is a reasoning reranker model that "thinks" before making relevance judgments. This 7B parameter model is trained from the Qwen2.5 7B base model and leverages test time compute to generate reasoning chains before deciding if a document is relevant to a query. Model Description rank1 introduces a novel approach to information retrieval by generating explicit reasoning chains before making relevance judgments. Unlike traditional rerankers that directly output scores, rank1: 1. Receives a query and document pair 2. Generates a reasoning chain within a ... section 3. Makes a binary relevance judgment ( true or false ) 4. Returns a confidence score based on the logits of the true/false tokens This approach helps the model break down complex relevance decisions into logical steps, improving performance across diverse retrieval tasks. Model Family Model Base Description : : : rank1 0.5b Qwen2.5 0.5B Smallest variant (0.5B parameters) rank1 1.5b Qwen2.5 1.5B Smaller variant (1.5B parameters) rank1 3b Qwen2.5 3B Smaller variant (3B parameters) rank1 7b Qwen2.5 7B Current model (7B paramete…
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