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Model: prithivMLmods/MemOperator-4B-f32-GGUF
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+base_model:
+- MemTensor/MemOperator-4B
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- 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):
+
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

config.json

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+{
+    "model_type": "qwen3"
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}