AdarshSingh7647/Eklav-8B-Reranker-CotGen
AdarshSingh7647/Eklav-8B-Reranker-CotGen is an 8 billion parameter reranker model based on Qwen/Qwen3-8B, specifically designed for passage reranking tasks. It utilizes a standard full trace CoT SFT (Chain-of-Thought Supervised Fine-Tuning) baseline, trained with the CotGen method to learn reasoning. This model excels at determining passage relevance, achieving an average nDCG@10 of 31.5 on the BRIGHT dataset, and is optimized for use as a pointwise reranker.
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Overview
AdarshSingh7647/Eklav-8B-Reranker-CotGen is an 8 billion parameter model built upon the Qwen/Qwen3-8B base, specifically fine-tuned for passage reranking. It employs a standard full trace Chain-of-Thought (CoT) Supervised Fine-Tuning (SFT) baseline, referred to as CotGen, where the model learns to continue a partial reasoning trace rather than imitating an end-to-end one. This approach aims to improve reasoning capabilities by conditioning the model's own reasoning on a teacher's partial trace.
Key Capabilities
- Passage Reranking: Designed to determine the relevance of a passage to a given query.
- Pointwise Reranker: Functions similarly to models like Rank1, generating a reasoning trace that concludes with
</think> trueor</think> false. - Performance: Achieves an average nDCG@10 of 31.5 on the BRIGHT dataset, indicating its effectiveness in ranking relevant passages.
Usage and Recommendations
This model is intended for use as a reranker where relevance is scored from the logits of the final true or false token. It's crucial to use a setup like vLLM with a stop string (e.g., </think> true, </think> false) rather than a fixed token budget for generation. This prevents the model from generating excessively long reasoning traces or degenerating into repetition, ensuring reliable output for reranking tasks.