Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Mungert/Caller-GGUF:# Run inference directly in the terminal:
llama-cli -hf Mungert/Caller-GGUF:Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Mungert/Caller-GGUF:# Run inference directly in the terminal:
./llama-cli -hf Mungert/Caller-GGUF:Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Mungert/Caller-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf Mungert/Caller-GGUF:Use Docker
docker model run hf.co/Mungert/Caller-GGUF:Caller GGUF Models
Model Generation Details
This model was generated using llama.cpp at commit 73e53dc8.
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
Caller (32B) is a robust model engineered for seamless integrations and optimized for managing complex tool-based interactions and API function calls. Its strength lies in precise execution, intelligent orchestration, and effective communication between systems, making it indispensable for sophisticated automation pipelines.
Model Details
- Architecture Base: Qwen2.5-32B
- Parameter Count: 32B
- License: Apache-2.0
Use Cases:
- Managing integrations between CRMs, ERPs, and other enterprise systems
- Running multi-step workflows with intelligent condition handling
- Orchestrating external tool interactions like calendar scheduling, email parsing, or data extraction
- Real-time monitoring and diagnostics in IoT or SaaS environments
Quantizations
GGUF format available here
License
Caller (32B) is released under the Apache-2.0 License. You are free to use, modify, and distribute this model in both commercial and non-commercial applications, subject to the terms and conditions of the license.
If you have questions or would like to share your experiences using Caller (32B), please connect with us on social media. We’re excited to see what you build—and how this model helps you innovate!
🚀 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.
Thank you! 😊
- Downloads last month
- 9
1-bit
2-bit
3-bit
4-bit
5-bit
6-bit
8-bit
16-bit
Model tree for Mungert/Caller-GGUF
Base model
Qwen/Qwen2.5-32B
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf Mungert/Caller-GGUF:# Run inference directly in the terminal: llama-cli -hf Mungert/Caller-GGUF: