huggingMarwa/functiongemma-270m-it-retail-actions-v2-temporal
The huggingMarwa/functiongemma-270m-it-retail-actions-v2-temporal model is a 0.3 billion parameter instruction-tuned causal language model, fine-tuned from Google's FunctionGemma-270m-it. This model specializes in retail-specific actions and temporal understanding, leveraging its small size for efficient deployment. It is optimized for function calling within retail contexts, making it suitable for applications requiring structured outputs based on user queries.
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Model Overview
This model, huggingMarwa/functiongemma-270m-it-retail-actions-v2-temporal, is a specialized fine-tuned version of the google/functiongemma-270m-it architecture. With 0.3 billion parameters and a context length of 32768 tokens, it is designed for efficient function calling in retail-specific scenarios.
Key Capabilities
- Function Calling: Optimized for generating structured outputs that can be used to invoke functions.
- Retail-Specific Actions: Fine-tuned to understand and respond to queries related to retail operations and customer interactions.
- Temporal Understanding: Enhanced capabilities for processing and responding to time-sensitive information within retail contexts.
- Instruction Following: Benefits from the instruction-tuned base model, allowing it to follow complex commands.
Training Details
The model was trained using the TRL (Transformer Reinforcement Learning) framework, specifically employing Supervised Fine-Tuning (SFT). This process adapted the base FunctionGemma model to excel in its specialized retail domain.
Good For
- Developing AI agents for retail customer service.
- Automating tasks requiring structured output in e-commerce platforms.
- Applications needing efficient, small-footprint models for function calling in retail environments.