MarktechPost·
In this tutorial, we use zeroentropy/zerank-2-reranker, a 4B Qwen3-based cross-encoder reranker, to improve retrieval quality. We start by setting up the runtime, loading the reranker, and understanding how it scores query-document pairs. Then, we move from simple pairwise scoring to a practical two-stage retrieve-and-rerank pipeline, where a fast bi-encoder first retrieves candidates and zerank-2 reranks […] The post Design a High-Precision Retrieve-and-Rerank Pipeline with ZeroEntropy Zerank-2 Reranker appeared first on MarkTechPost.
Read full articleEnterprise Document Intelligence [Vol. 1 #2bis] Why stacking a reranker on top of weak retrieval doesn’t save it, what cross-encoders actually fix vs what they don’t, and where the editorial position of the series lands. The post Rerankers Aren’t Magic Either: When the Cross-Encoder Layer Is Worth the Cost appeared first on Towards Data Science.
Long-chain reasoning is one of the most compute-intensive tasks in modern large language models. When a model like DeepSeek-R1 or Qwen3 works through a complex math problem, it can generate tens of thousands of tokens before arriving at an answer. Every one of those tokens must be stored in what is called the KV cache […] The post Researchers from MIT, NVIDIA, and Zhejiang University Propose TriAttention: A KV Cache Compression Method That Matches Full Attention at 2.5× Higher Throughput appeared first on MarkTechPost.
