MemOperator-4B-f32-GGUF/README.md

license, base_model, language, pipeline_tag, library_name, tags

license	base_model	language	pipeline_tag	library_name	tags
apache-2.0	MemTensor/MemOperator-4B	en	text-generation	transformers	text-generation-inference

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

Model File name	Size	QuantType
MemOperator-4B.BF16.gguf	8.05 GB	BF16
MemOperator-4B.F16.gguf	8.05 GB	F16
MemOperator-4B.F32.gguf	16.1 GB	F32
MemOperator-4B.Q2_K.gguf	1.67 GB	Q2_K
MemOperator-4B.Q3_K_L.gguf	2.24 GB	Q3_K_L
MemOperator-4B.Q3_K_M.gguf	2.08 GB	Q3_K_M
MemOperator-4B.Q3_K_S.gguf	1.89 GB	Q3_K_S
MemOperator-4B.Q4_K_M.gguf	2.5 GB	Q4_K_M
MemOperator-4B.Q4_K_S.gguf	2.38 GB	Q4_K_S
MemOperator-4B.Q5_K_M.gguf	2.89 GB	Q5_K_M
MemOperator-4B.Q5_K_S.gguf	2.82 GB	Q5_K_S
MemOperator-4B.Q6_K.gguf	3.31 GB	Q6_K
MemOperator-4B.Q8_0.gguf	4.28 GB	Q8_0

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):