IAAR-Shanghai/MemReranker-4B
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:Apr 27, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Warm
IAAR-Shanghai/MemReranker-4B is a 4 billion parameter model developed by IAAR-Shanghai, designed for reranking tasks. With a context length of 32768 tokens, it specializes in efficiently reordering sequences based on relevance. This model is optimized for applications requiring high-throughput and accurate ranking of information.
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Overview
IAAR-Shanghai/MemReranker-4B is a 4 billion parameter model developed by IAAR-Shanghai. It is specifically engineered for reranking tasks, focusing on efficiently reordering sequences to improve relevance and retrieval accuracy. The model supports a substantial context length of 32768 tokens, allowing it to process and rerank long sequences of information.
Key Capabilities
- Efficient Reranking: Designed to reorder lists or sequences based on a given criteria, enhancing the relevance of results.
- Large Context Window: Utilizes a 32768-token context length, enabling it to handle extensive inputs for complex reranking scenarios.
- Optimized for Relevance: Focuses on improving the order of items to present the most pertinent information first.
Good For
- Information Retrieval Systems: Enhancing search results by reranking documents or passages.
- Recommendation Engines: Improving the order of recommended items based on user preferences or context.
- Long Document Processing: Reranking sections or summaries within very long texts to highlight key information.