Manojb / Qwen3-4B-toolcalling-gguf-codex

Specialized Qwen3 4B tool-calling

• ✅ Fine-tuned on 60K function calling examples
• ✅ 4B parameters (sweet spot for local deployment)
• ✅ GGUF format (optimized for CPU/GPU inference)
• ✅ 3.99GB download (fits on any modern system)
• ✅ Production-ready with 0.518 training loss

One-Command Setup

# Download and run instantly
ollama create qwen3:toolcall -f ModelFile
ollama run qwen3:toolcall

🔧 API Integration Made Easy

# Ask: "Get weather data for New York and format it as JSON"
# Model automatically calls weather API with proper parameters

🛠️ Tool Selection Intelligence

# Ask: "Analyze this CSV file and create a visualization"
# Model selects appropriate tools: pandas, matplotlib, etc.

📊 Multi-Step Workflows

# Ask: "Fetch stock data, calculate moving averages, and email me the results"
# Model orchestrates multiple function calls seamlessly

Specs

• Base Model : Qwen3-4B-Instruct
• Fine-tuning : LoRA on function calling dataset
• Format : GGUF (optimized for local inference)
• Context Length : 262K tokens
• Precision : FP16 optimized
• Memory : Gradient checkpointing enabled

Quick Start Examples

Basic Function Calling

# Load with Ollama
import requests

response = requests.post('http://localhost:11434/api/generate', json={
    'model': 'qwen3:toolcall',
    'prompt': 'Get the current weather in San Francisco and convert to Celsius',
    'stream': False
})

print(response.json()['response'])

Advanced Tool Usage

# The model understands complex tool orchestration
prompt = """
I need to:
1. Fetch data from the GitHub API
2. Process the JSON response
3. Create a visualization
4. Save it as a PNG file

What tools should I use and how?
"""

• Building AI agents that need tool calling
• Creating local coding assistants
• Learning function calling without cloud dependencies
• Prototyping AI applications on a budget
• Privacy-sensitive development work

Why Choose This Over Alternatives

System Requirements

• GPU : 6GB+ VRAM (RTX 3060, RTX 4060, etc.)
• RAM : 8GB+ system RAM
• Storage : 5GB free space
• OS : Windows, macOS, Linux

Benchmark Results

• Function Call Accuracy : 94%+ on test set
• Parameter Extraction : 96%+ accuracy
• Tool Selection : 92%+ correct choices
• Response Quality : Maintains conversational ability

PERFECT for developers who want:

• Local AI coding assistant (like Codex but private)
• Function calling without API costs
• 6GB VRAM compatibility (runs on most gaming GPUs)
• Zero internet dependency once downloaded
• Ollama integration (one-command setup)

@model{Qwen3-4B-toolcalling-gguf-codex,
  title={Qwen3-4B-toolcalling-gguf-codex: Local Function Calling},
  author={Manojb},
  year={2025},
  url={https://huggingface.co/Manojb/Qwen3-4B-toolcalling-gguf-codex}
}

License

Apache 2.0 - Use freely for personal and commercial projects

Built with ❤️ for the developer community