KickItLikeShika/qwen2.5-7b-infosft-tooluse
KickItLikeShika/qwen2.5-7b-infosft-tooluse is a 7.6 billion parameter language model based on the Qwen2.5 architecture, fine-tuned for tool use capabilities. It leverages the InfoSFT (Information-Aware Token Weighting) method to enhance learning efficiency and was trained on a specialized tool-use dataset. This model is designed to excel in tasks requiring external tool integration and scored 67% on its evaluation set.
Loading preview...
Model Overview
KickItLikeShika/qwen2.5-7b-infosft-tooluse is a 7.6 billion parameter model built upon the Qwen2.5 architecture, specifically fine-tuned for tool-use applications. This model incorporates the InfoSFT (Information-Aware Token Weighting) method, which aims to improve learning by focusing on more informative tokens and reducing the impact of less relevant ones. The training utilized a dedicated tool-use dataset, originally released with the Self-Distillation Fine-tuning (SDFT) framework.
Key Capabilities
- Enhanced Tool Use: Specifically trained on a tool-use dataset, making it proficient in scenarios requiring interaction with external functions or APIs.
- InfoSFT Integration: Benefits from Information-Aware Token Weighting, a technique designed to optimize the learning process by emphasizing critical information.
- Performance: Achieved a 67% score on its evaluation set, indicating its effectiveness in tool-use tasks.
Good For
- Function Calling: Ideal for applications where the model needs to identify and call external tools or APIs based on user prompts.
- Agentic Workflows: Suitable for building AI agents that can leverage various tools to accomplish complex tasks.
- Research in Tool Learning: Provides a base for further experimentation and development in the field of language models interacting with tools.