Baby_Grok3-1.5b-iQ4_K_M-GGUF
| Property | Value |
|---|---|
| Parameter Count | 1.5B |
| Model Type | Function-Calling Language Model |
| Architecture | Based on Qwen 2.5 coder series |
| Author | IntelligentEstate |
| Hugging Face URL | https://huggingface.co/IntelligentEstate/Baby_Grok3-1.5b-iQ4_K_M-GGUF |
What is Baby_Grok3-1.5b-iQ4_K_M-GGUF?
Baby_Grok3 is a groundbreaking small-scale language model specifically designed for edge devices, notably optimized for the Orange Pi Zero system. Despite its compact 1.5B parameter size, it demonstrates remarkable capabilities that rival much larger models, particularly in function-calling tasks. The model has been converted using a custom importance matrix to GGUF format, making it highly efficient for real-world applications.
Implementation Details
The model is built upon the Hammer 2.1 architecture and fine-tuned from the Qwen 2.5 coder series. It implements advanced features through specialized optimization techniques and shows exceptional performance on the Berkeley Function-Calling Leaderboard (BFCL-v3), surpassing models many times its size.
- Multi-Step Function Calling capability for complex task handling
- Context-aware Multi-Turn Function Calling for natural conversations
- Enhanced irrelevant information filtering
- Optimized for edge device deployment
Core Capabilities
- Superior function-calling abilities comparable to larger models
- Efficient performance on resource-constrained devices
- Advanced reasoning and tool utilization
- Multiple in-turn tool use functionality
- Exceptional insight generation despite small size
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to perform at par with much larger models while maintaining a small footprint makes it unique. It specifically excels in function-calling tasks and can run efficiently on edge devices like the Orange Pi Zero.
Q: What are the recommended use cases?
The model is ideal for edge computing applications requiring sophisticated function-calling capabilities, particularly in resource-constrained environments. It's especially suitable for creating flash drive functional assistants and embedded AI applications.
