Qrzysztof/functiongemma-270m-it-ecommerce-chat
Qrzysztof/functiongemma-270m-it-ecommerce-chat is a 0.3 billion parameter instruction-tuned causal language model, fine-tuned from Google's functiongemma-270m-it. This model specializes in e-commerce chat applications, leveraging its base architecture for function calling capabilities. It is optimized for conversational tasks within an e-commerce context, providing a compact solution for specific domain interactions.
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Overview
This model, Qrzysztof/functiongemma-270m-it-ecommerce-chat, is a fine-tuned version of Google's functiongemma-270m-it model. It has been specifically adapted for e-commerce chat applications, building upon the base model's function calling capabilities. The fine-tuning process utilized the TRL (Transformers Reinforcement Learning) library, indicating a focus on optimizing conversational interactions.
Key Characteristics
- Base Model: Fine-tuned from
google/functiongemma-270m-it. - Parameter Count: 0.3 billion parameters, making it a relatively compact model.
- Training Framework: Trained using SFT (Supervised Fine-Tuning) with the TRL library.
- Intended Use: Optimized for conversational tasks within an e-commerce domain, likely involving function calls for actions like product search, order status, or customer support.
When to Use This Model
This model is particularly suitable for:
- Developing chatbots for e-commerce websites.
- Automating customer service interactions related to online shopping.
- Applications requiring a compact language model with function calling capabilities tailored for retail environments.