Qrzysztof/functiongemma-270m-it-prepaid-cards
Qrzysztof/functiongemma-270m-it-prepaid-cards is a 0.3 billion parameter instruction-tuned language model, fine-tuned by Qrzysztof from Google's functiongemma-270m-it. This model leverages a 32768 token context length and was trained using the TRL framework. It is designed for text generation tasks, building upon the function-calling capabilities of its base model.
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Model Overview
This model, Qrzysztof/functiongemma-270m-it-prepaid-cards, is a specialized fine-tune of Google's functiongemma-270m-it model. Developed by Qrzysztof, it retains the compact 0.3 billion parameter size and a substantial 32768 token context window, making it suitable for applications requiring efficient processing of longer inputs.
Key Characteristics
- Base Model: Built upon
google/functiongemma-270m-it, inheriting its core architecture and function-calling capabilities. - Training Method: Fine-tuned using the Supervised Fine-Tuning (SFT) approach with the TRL library.
- Frameworks: Utilizes TRL (version 1.9.2), Transformers (version 5.13.1), PyTorch (version 2.11.0+cu128), Datasets (version 5.0.1), and Tokenizers (version 0.22.2).
Use Cases
This model is primarily intended for text generation tasks, particularly those that can benefit from the underlying function-calling design of the functiongemma family. Its small size makes it efficient for deployment in environments with limited computational resources, while its large context window allows for handling detailed prompts and generating comprehensive responses.