Mungert/mem-agent-GGUF

mem-agent GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit 1d0125bc.

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Quantization Beyond the IMatrix

I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.

In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the --tensor-type option in llama.cpp to manually "bump" important layers to higher precision. You can see the implementation here:
👉 Layer bumping with llama.cpp

While this does increase model file size, it significantly improves precision for a given quantization level.

I'd love your feedback—have you tried this? How does it perform for you?

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Click here to get info on choosing the right GGUF model format

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mem-agent

Based on 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:

# 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

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, 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

# 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)

# Jane Doe
- relationship: Mother
- birth_date: 1965-01-01
- birth_location: New York, USA

# 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, Zheng et al., 2025

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🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

• TurboLLM (GPT-4.1-mini)
• HugLLM (Hugginface Open-source models)
• TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

• Function calling against live network services
• How small can a model go while still handling:
  • Automated Nmap security scans
  • Quantum-readiness checks
  • Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

• ✅ Zero-configuration setup
• ⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
• 🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

• **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
• Create custom cmd processors to run .net code on Quantum Network Monitor Agents
• Real-time network diagnostics and monitoring
• Security Audits
• Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

• 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test:

1. "Give me info on my websites SSL certificate"
2. "Check if my server is using quantum safe encyption for communication"
3. "Run a comprehensive security audit on my server"
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊