license, language, base_model, pipeline_tag, library_name, tags
| license | language | base_model | pipeline_tag | library_name | tags | ||||
|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 |
|
|
text-generation | transformers |
|
mem-agent-f32-gguf
driaforall/mem-agent is an agentic model based on Qwen3-4B-Thinking-2507, fine-tuned using GSPO (Zheng et al., 2025) to interact with an Obsidian-inspired, markdown-based memory system for advanced retrieval, updating, and clarification tasks. It is structured around agentic scaffolding that leverages dedicated tags and tool APIs for file and directory operations, supporting memory filtering and obfuscation, and evaluated on the md-memory-bench where it outperformed most open and closed models except qwen/qwen3-235b-a22b-thinking-2507, with an overall benchmark score of 0.75. The model is designed for use as an MCP server or standalone, and relies on linked markdown files to manage user and entity data, enabling seamless, flexible document-like memory manipulation for agentic or personal assistant scenarios.
Model Files
| File Name | Quant Type | File Size |
|---|---|---|
| mem-agent.BF16.gguf | BF16 | 8.05 GB |
| mem-agent.F16.gguf | F16 | 8.05 GB |
| mem-agent.F32.gguf | F32 | 16.1 GB |
| mem-agent.Q2_K.gguf | Q2_K | 1.67 GB |
| mem-agent.Q3_K_L.gguf | Q3_K_L | 2.24 GB |
| mem-agent.Q3_K_M.gguf | Q3_K_M | 2.08 GB |
| mem-agent.Q3_K_S.gguf | Q3_K_S | 1.89 GB |
| mem-agent.Q4_K_M.gguf | Q4_K_M | 2.5 GB |
| mem-agent.Q4_K_S.gguf | Q4_K_S | 2.38 GB |
| mem-agent.Q5_K_M.gguf | Q5_K_M | 2.89 GB |
| mem-agent.Q5_K_S.gguf | Q5_K_S | 2.82 GB |
| mem-agent.Q6_K.gguf | Q6_K | 3.31 GB |
| mem-agent.Q8_0.gguf | Q8_0 | 4.28 GB |
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):
