gaoj0017/functiongemma-270m-it-simple-tool-calling
The gaoj0017/functiongemma-270m-it-simple-tool-calling model is a 270 million parameter instruction-tuned variant of Google's FunctionGemma architecture, specifically fine-tuned for simple tool-calling tasks. Developed by gaoj0017, this model leverages a 32K context length and is optimized for integrating external functions and APIs into its responses. It is designed for applications requiring efficient and straightforward function invocation capabilities.
Loading preview...
Model Overview
The gaoj0017/functiongemma-270m-it-simple-tool-calling model is a specialized language model derived from Google's functiongemma-270m-it architecture. It has been further fine-tuned by gaoj0017 using the TRL (Transformers Reinforcement Learning) library, focusing on enhancing its ability to perform simple tool-calling tasks.
Key Capabilities
- Function Calling: Optimized for identifying and invoking external functions or APIs based on user prompts.
- Instruction Following: Benefits from its instruction-tuned base, allowing it to understand and execute specific commands.
- Compact Size: With 270 million parameters, it offers a balance between performance and computational efficiency, making it suitable for deployment in resource-constrained environments.
- Extended Context: Supports a context length of 32,768 tokens, enabling it to process longer inputs and maintain conversational coherence for tool-use scenarios.
Training Details
The model underwent Supervised Fine-Tuning (SFT) using the TRL framework. This process adapted the base functiongemma-270m-it model to excel in simple tool-calling applications. The training utilized specific versions of libraries including TRL 1.13.0, Transformers 5.16.1, and PyTorch 2.11.0+cu128.
Good for
- Simple Tool Integration: Ideal for developers looking to integrate basic function-calling capabilities into their applications without the overhead of larger models.
- Resource-Efficient Deployments: Its small parameter count makes it suitable for edge devices or applications with limited computational resources.
- Prototyping: Excellent for quickly prototyping and testing tool-calling functionalities.