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README.md

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+
+---
+
+pipeline_tag: text-generation
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/mem-agent-GGUF
+This is quantized version of [driaforall/mem-agent](https://huggingface.co/driaforall/mem-agent) created using llama.cpp
+
+# Original Model Card
+
+# mem-agent
+
+Based on [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507), this model was trained using GSPO (Zheng et al., 2025) over an agent scaffold that is built around an Obisidian-like memory system and the tools required to interact with it. The model was trained on the following subtasks:
+- Retrieval: Retrieving relevant information when needed from the memory system. In this subtask, we also trained the model on filtering the retrieved information and/or obfuscating it completely.
+- Updating: Updating the memory system with new information.
+- Clarification: Asking for clarification when the user query is not clear/contradicting with the information in the memory system.
+
+The tools in the scaffold are:
+```markdown
+# File Operations
+create_file(file_path: str, content: str = "") -> bool  # Auto-creates parent directories
+update_file(file_path: str, old_content: str, new_content: str) -> Union[bool, str] # Returns True or error message
+read_file(file_path: str) -> str
+delete_file(file_path: str) -> bool
+check_if_file_exists(file_path: str) -> bool
+
+# Directory Operations
+create_dir(dir_path: str) -> bool
+list_files() -> str  # Shows tree structure of current working directory
+check_if_dir_exists(dir_path: str) -> bool
+
+# Utilities
+get_size(file_or_dir_path: str) -> int  # Bytes; empty = total memory size
+go_to_link(link_string: str) -> bool
+```
+
+In the scaffold, the model uses `<think>`, `<python>` and `<reply>` tags to structure its response. Using `<reply>` only when it's done interacting with the memory. The `<python>` block is executed in a sandbox with the tools and the results of the code block are returned in a `<result>` tag to the model, forming the agentic loop.
+
+The model is also trained to be able to handle optional filters given by the user in between <filter> tags after the user query. These filters are used to filter the retrieved information and/or obfuscate it completely.
+
+
+## Benchmark
+
+We evaluated this model and a few other open & closed ones on our benchmark, **md-memory-bench**. We used o3 from OpenAI as the judge. All the other models except driaforall/mem-agent and Qwen/Qwen3-4B-Thinking-2507 were used through OpenRouter.s
+
+| Model | Retrieval | Update | Clarification | Filter | Overall |
+|-------|-----------|--------|---------------|--------|---------|
+| qwen/qwen3-235b-a22b-thinking-2507 | 0.9091 | 0.6363 | 0.4545 | 1 | 0.7857 |
+| driaforall/mem-agent | 0.8636 | 0.7272 | 0.3636 | 0.9167 | 0.75 |
+| z-ai/glm-4.5 | 0.7727 | 0.8181 | 0.3636 | 0.9167 | 0.7321 |
+| deepseek/deepseek-chat-v3.1 | 0.6818 | 0.5454 | 0.5454 | 0.8333 | 0.6607 |
+| google/gemini-2.5-pro | 0.7273 | 0.4545 | 0.2727 | 1 | 0.6429 |
+| google/gemini-2.5-flash | 0.7727 | 0.3636 | 0.2727 | 0.9167 | 0.625 |
+| openai/gpt-5 | 0.6818 | 0.5454 | 0.2727 | 0.9167 | 0.625 |
+| anthropic/claude-opus-4.1 | 0.6818 | 0 | 0.8181 | 0.5833 | 0.5536 |
+| Qwen/Qwen3-4B-Thinking-2507 | 0.4545 | 0 | 0.2727 | 0.75 | 0.3929 |
+| moonshotai/kimi-k2 | 0.3181 | 0.2727 | 0.1818 | 0.6667 | 0.3571 |
+
+Our model, with only 4B parameters, is only second on the benchmark, beating all the open & closed models except for qwen/qwen3-235b-a22b-thinking-2507. The model achieves an overall score of 0.75, a significant improvement over the 0.3929 of the base Qwen model.
+
+## Usage
+
+The model, while can be used on its own, is recommended to be used as an MCP server to a bigger model, which can then be used to interact with the memory system. For this, you can check [our repo](https://github.com/firstbatchxyz/mem-agent-mcp/), which contains instructions for both an MCP setup and a cli standalone model usage.
+
+### Memory
+
+The model uses a markdown based memory system with links, inspired by Obsidian. The general structure of the memory is:
+```
+memory/
+    ├── user.md
+    └── entities/
+        └── [entity_name_1].md
+        └── [entity_name_2].md
+        └── ...
+```
+
+- `user.md` is the main file that contains information about the user and their relationships, accompanied by links to the enity file in the format of `[[entities/[entity_name].md]]` per relationship. The link format should be followed strictly.
+- `entities/` is the directory that contains the entity files.
+- Each entity file follows the same structure as `user.md`.
+- Modifying the memory manually does not require restarting the MCP server.
+
+### Example user.md
+
+```markdown
+# User Information
+- user_name: John Doe
+- birth_date: 1990-01-01
+- birth_location: New York, USA
+- living_location: Enschede, Netherlands
+- zodiac_sign: Aquarius
+
+## User Relationships
+- company: [[entities/acme_corp.md]]
+- mother: [[entities/jane_doe.md]]
+```
+
+### Example entity files (jane_doe.md and acme_corp.md)
+
+```markdown
+# Jane Doe
+- relationship: Mother
+- birth_date: 1965-01-01
+- birth_location: New York, USA
+```
+
+```markdown 
+# Acme Corporation
+- industry: Software Development
+- location: Enschede, Netherlands
+```
+
+The model is trained on this memory standard and any fruitful use should be on a memory system that follows this standard. We have a few memory export tools for different sources like ChatGPT, Notion, etc. in our mcp server repo.
+
+## References: 
+- [GSPO](https://arxiv.org/pdf/2507.18071), Zheng et al., 2025