Devloop4545/functiongemma-270m-it-simple-tool-calling
Devloop4545/functiongemma-270m-it-simple-tool-calling is a 0.3 billion parameter language model fine-tuned from Google's functiongemma-270m-it. This model specializes in tool-calling capabilities, leveraging its base architecture for efficient function invocation. It was trained using TRL and supports a context length of 32768 tokens, making it suitable for applications requiring compact yet capable function-calling LLMs.
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Model Overview
This model, Devloop4545/functiongemma-270m-it-simple-tool-calling, is a fine-tuned variant of Google's functiongemma-270m-it model, specifically optimized for tool-calling tasks. With 0.3 billion parameters, it offers a compact solution for integrating LLM-powered function invocation into applications. The model was developed using the TRL (Transformers Reinforcement Learning) library, indicating a focus on efficient and effective fine-tuning.
Key Capabilities
- Function Calling: Specialized in understanding and generating calls to external tools or functions based on user prompts.
- Compact Size: At 0.3 billion parameters, it is designed for efficient deployment and lower computational overhead compared to larger models.
- Extended Context: Supports a context length of 32768 tokens, allowing for processing longer inputs relevant to tool-calling scenarios.
Training Details
The model underwent a Supervised Fine-Tuning (SFT) process using TRL, version 1.12.0. This training approach enhances its ability to accurately interpret user intent and formulate appropriate tool calls. The base model, google/functiongemma-270m-it, provides a strong foundation for these capabilities.