MemOperator-4B-f32-GGUF

MemOperator-4B by MemTensor is a specialized causal language model designed for efficient memory operations within the MemOS system. It excels in memory extraction, integration, and updating while enabling local-only deployment for environments without internet access. Derived from the Qwen3-4B architecture and fine-tuned via supervised learning on both human-annotated and generated data, this 4 billion parameter model supports both English and Chinese, and processes long contexts up to 32,768 tokens.

It offers fast, low-resource memory management that outperforms comparably sized open models like GPT-4o-mini, making it ideal for real-time, cost-effective memory tasks in conversational and document settings. MemOperator-4B is designed to seamlessly extract high-quality memories and organize them for enhanced long-term coherence in applications such as MemOS, supporting memory-centric AI workflows with strong multilingual capabilities and robust system performance.

Model Files

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