Saad4web/functiongemma-270m-it-simple-tool-calling
The Saad4web/functiongemma-270m-it-simple-tool-calling model is a 0.3 billion parameter instruction-tuned language model, fine-tuned from google/functiongemma-270m-it. It is specifically optimized for simple tool-calling tasks, leveraging its compact size for efficient deployment. This model is designed for applications requiring function calling capabilities with a smaller computational footprint.
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Overview
This model, Saad4web/functiongemma-270m-it-simple-tool-calling, is a specialized instruction-tuned language model with 0.3 billion parameters. It is a fine-tuned variant of the google/functiongemma-270m-it base model, developed using the TRL (Transformers Reinforcement Learning) framework.
Key Capabilities
- Efficient Tool Calling: Optimized for performing simple tool-calling tasks, making it suitable for integrating with external functions or APIs.
- Compact Size: With only 0.3 billion parameters, it offers a lightweight solution for applications where computational resources or latency are critical.
- Instruction Following: Inherits instruction-following capabilities from its base model, allowing it to understand and execute user prompts effectively.
Training Details
The model was trained using Supervised Fine-Tuning (SFT) with the TRL library. The training environment utilized TRL 1.8.0, Transformers 5.13.1, Pytorch 2.11.0+cu128, Datasets 5.0.0, and Tokenizers 0.22.2.
Good For
- Applications requiring lightweight function-calling models.
- Edge deployments or scenarios with limited computational resources.
- Prototyping and development of tool-augmented language model systems.