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?
GLM-ASR-Nano-2512 is a robust, open-source speech recognition model with 1.5B parameters. Designed for
real-world complexity, it outperforms OpenAI Whisper V3 on multiple benchmarks while maintaining a compact size.
Key capabilities include:
Exceptional Dialect Support:
Beyond standard Mandarin and English, the model is highly optimized for Cantonese (粤语) and other dialects,
effectively bridging the gap in dialectal speech recognition.
Low-Volume Speech Robustness:
Specifically trained for "Whisper/Quiet Speech" scenarios. It captures and accurately transcribes extremely
low-volume audio that traditional models often miss.
SOTA Performance:
Achieves the lowest average error rate (4.10) among comparable open-source models, showing significant advantages
in Chinese benchmarks (Wenet Meeting, Aishell-1, etc..).
Benchmark
We evaluated GLM-ASR-Nano against leading open-source and closed-source models. The results demonstrate that *
GLM-ASR-Nano (1.5B)* achieves superior performance, particularly in challenging acoustic environments.
Benchmark results
Notes:
Wenet Meeting reflects real-world meeting scenarios with noise and overlapping speech.
Aishell-1 is a standard Mandarin benchmark.
Inference
GLM-ASR-Nano-2512 can be easily integrated using the transformers library.
We will support transformers 5.x as well as inference frameworks such as vLLM and SGLang.
you can check more code in Github.
🚀 If you find these models useful
Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:
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.
