hsmehta012/functiongemma-270m-it-simple-tool-calling
The hsmehta012/functiongemma-270m-it-simple-tool-calling model is a 0.3 billion parameter instruction-tuned causal language model, fine-tuned from Google's functiongemma-270m-it. With a context length of 32768 tokens, this model is specifically optimized for simple tool-calling tasks. It is designed for efficient function generation and interaction within constrained environments.
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Overview
This model, hsmehta012/functiongemma-270m-it-simple-tool-calling, is a fine-tuned variant of Google's functiongemma-270m-it model. It features 0.3 billion parameters and supports a substantial context length of 32768 tokens. The fine-tuning process utilized the TRL library, indicating a focus on reinforcement learning from human feedback or similar techniques to enhance its capabilities.
Key Capabilities
- Function Calling: Optimized for generating and interacting with simple tool calls.
- Instruction Following: Inherits instruction-tuned capabilities from its base model, allowing it to respond to user prompts effectively.
- Efficient Deployment: Its relatively small size (0.3B parameters) makes it suitable for applications where computational resources are a consideration.
Training Details
The model was trained using Supervised Fine-Tuning (SFT) with the TRL framework (version 1.10.0). The training environment included Transformers 5.13.1, Pytorch 2.11.0+cu128, Datasets 5.0.1, and Tokenizers 0.22.2.
Good For
- Applications requiring lightweight function-calling models.
- Integrating simple tool-use capabilities into conversational agents or automated workflows.
- Scenarios where a balance between model size and specialized function-calling performance is crucial.