KickItLikeShika/qwen-2.5-7b-taid-tooluse
KickItLikeShika/qwen-2.5-7b-taid-tooluse is a 7.6 billion parameter language model based on the Qwen2.5 architecture, fine-tuned using Temporally Adaptive Interpolated Distillation (TAID). This model specializes in tool use, having been trained on a dedicated tool-use dataset. It achieves a 62.8% score on its evaluation set, making it suitable for applications requiring function calling and external tool interaction.
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Model Overview
KickItLikeShika/qwen-2.5-7b-taid-tooluse is a 7.6 billion parameter model built upon the Qwen2.5-7B-Instruct architecture. Its key differentiator is its training methodology: Temporally Adaptive Interpolated Distillation (TAID). This technique uses a larger Qwen2.5-32B-Instruct model as a teacher to distill knowledge into the smaller 7.6B parameter student model.
Key Capabilities
- Tool Use Specialization: The model is specifically trained on a tool-use dataset, enhancing its ability to understand and execute function calls or interact with external tools.
- Performance: Achieved a 62.8% score on its evaluation set, indicating proficiency in tool-use scenarios.
- Efficient Knowledge Transfer: Leverages TAID for effective knowledge transfer from a larger, more capable teacher model, aiming for strong performance in a smaller footprint.
Use Cases
This model is particularly well-suited for applications requiring:
- Function Calling: Integrating with APIs or external services where the model needs to generate structured calls.
- Automated Workflows: Tasks that involve using tools to retrieve information, perform actions, or interact with systems.
- Agentic AI Systems: Developing AI agents that can plan and execute multi-step tasks by utilizing various tools.