Mistral-Crab-DPO GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit 8846aace.

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?

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

Model Card for Mistral-Crab-DPO

Large language models (LLMs) struggle to follow instructions with complex constraints in format, length, etc. Following the conventional instruction-tuning practice, previous works conduct post-training on complex instruction-response pairs generated by feeding complex instructions to advanced LLMs. However, even advanced LLMs cannot follow complex instructions well, thus limiting the quality of generated data. In this work, we find that existing datasets inherently contain implicit complex constraints and propose a novel data generation technique, constraint back-translation. Specifically, we take the high-quality instruction-response pairs in existing datasets and only adopt advanced LLMs to add complex constraints already met by the responses to the instructions, which naturally reduces costs and data noise. In the experiments, we adopt Llama3-70B-Instruct to back-translate constraints and create a high-quality complex instruction-response dataset, named CRAB. We present that post-training on CRAB improves multiple backbone LLMs' complex instruction-following ability, evaluated on extensive instruction-following benchmarks. We further find that constraint back-translation also serves as a useful auxiliary training objective in post-training.

📖 Paper: Constraint Back-translation Improves Complex Instruction Following of Large Language Models
🦀 Github: THU/Crab

Model Performance

Models	BaseModel	IFEval	FollowBench(HSR)			AVG
		AVG	L1-L2	L3-L5	AVG
GPT-3.5-turbo	GPT	66.3	74.2	61	66.2	66.3
GPT-4	GPT	81.3	80.4	69.4	73.8	77.6
Vicuna-13b-V1.5	Llama2	50.3	66.3	39.8	50.4	50.4
WizardLM-13B-V1.2	Llama2	51.4	56.5	36.9	44.7	48
Conifer-13B	Llama2	50.2	57.1	40.3	47	48.6
Zephyr-7B-beta	Mistral	45.4	54.8	38.2	44.8	45.1
Conifer-7B	Mistral	53.9	51.9	40.2	44.9	49.4
Conifer-7B-DPO	Mistral	55.7	57	45.4	50	52.9
Llama3 8B	Llama3	31.4	6.8	8.2	7.6	19.5
Llama3-crab	Llama3	46.9	51.2	26.7	36.5	41.7
Llama3-crab + DPO	Llama3	49.7	56.8	38.1	45.5	47.6
Mistral 7B	Mistral	25.2	15.5	6.5	10.1	17.7
Mistral-crab	Mistral	54.5	59.2	32.8	43.3	48.9
Mistral-crab + DPO	Mistral	59.4	59.9	42.5	49.4	54.4

Model Description

Developed by: Yunjia Qi, Hao Peng, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li
Model type: Text Generation
Language(s) (NLP): English
Finetuned from model [optional]: Mistral-7B-v0.3

🚀 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:

"Give me info on my websites SSL certificate"
"Check if my server is using quantum safe encyption for communication"
"Run a comprehensive security audit on my server"
'"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! 😊