mem-agent 4B - Q4_K_M GGUF
This is a 4-bit GGUF quantization of driaforall/mem-agent, a persistent memory agent trained with online RL.
Model Description
mem-agent is a 4B parameter language model based on Qwen3-4B-Thinking-2507, trained using GSPO (Generalized Supervised Policy Optimization) to interact with a markdown-based memory system inspired by Obsidian.
This GGUF conversion enables:
- β LM Studio compatibility - Run the model locally with an intuitive GUI
- β Windows support - Part of a broader mem-agent Windows porting project
- β CPU/GPU inference - Optimized for various hardware configurations
- β Reduced memory footprint - ~2GB model size with minimal performance loss
Original Model
The original model was developed by driaforall and achieves impressive results:
- 75% overall score on md-memory-bench
- Rivals models 50x its size on memory tasks
- Trained on three core capabilities: Retrieval, Updating, and Clarification
Read the full technical blog post: mem-agent: Persistent, Human Readable Memory Agent
Quantization Details
- Format: GGUF (Q4_K_M)
- Precision: 4-bit quantization
- Size: ~2GB
- Performance: Minimal degradation compared to full precision (original 4-bit MLX version: 76.8% overall score)
Why This Port?
The original mem-agent MCP server was designed for Mac and Linux environments. This GGUF conversion is part of a Windows porting project to make mem-agent accessible to a broader audience through:
- LM Studio integration for easy local deployment
- Cross-platform compatibility
- Standard GGUF toolchain support (llama.cpp, Ollama, etc.)
πͺ Windows Port Project
This model is part of the Windows porting effort of mem-agent:
- Repository: mem-agent-mcp-Windows
- Goal: Enable mem-agent functionality on Windows systems
- Integration: Compatible with LM Studio and other GGUF-based tools
Usage
LM Studio
- Download the GGUF file
- Load it in LM Studio
- Configure the model with appropriate system prompts for memory agent functionality
llama.cpp
./main -m mem-agent-4B-Q4-K-M.gguf -p "Your prompt here" -n 512
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Base model
Qwen/Qwen3-4B-Thinking-2507