SmolQwen/functiongemma-270m-it-simple-tool-calling
SmolQwen/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 simple tool-calling tasks, leveraging its compact size for efficient deployment. It is designed for applications requiring function-calling capabilities within a 32768 token context length. The model's primary strength lies in its ability to interpret and execute tool-use instructions effectively.
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Model Overview
SmolQwen/functiongemma-270m-it-simple-tool-calling is a compact 0.3 billion parameter model, derived from the google/functiongemma-270m-it architecture. It has been specifically fine-tuned using the TRL (Transformers Reinforcement Learning) framework to enhance its capabilities in simple tool-calling scenarios.
Key Capabilities
- Simple Tool Calling: Optimized for interpreting and responding to prompts that require the invocation of external functions or tools.
- Efficient Deployment: Its small parameter count (270M) makes it suitable for resource-constrained environments or applications where quick inference is critical.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for complex tool-use instructions and multi-turn interactions.
Training Details
The model was trained using Supervised Fine-Tuning (SFT) with the TRL library (version 1.10.0), built upon Transformers (version 5.15.0) and PyTorch (version 2.11.0+cu128). This training methodology focuses on aligning the model's outputs with desired tool-calling behaviors.
Use Cases
This model is particularly well-suited for applications requiring lightweight, function-calling LLMs, such as:
- Automated task execution based on natural language commands.
- Integrating with external APIs or services through tool-use prompts.
- Developing intelligent agents that can interact with digital environments by calling specific functions.