dzakyalfitra/FoodExtract-gemma-3-270m-fine-tune-v1
The dzakyalfitra/FoodExtract-gemma-3-270m-fine-tune-v1 is a 0.3 billion parameter language model, fine-tuned from Google's Gemma-3-270m-it architecture. This model was trained using the TRL framework, indicating an optimization for specific tasks through supervised fine-tuning (SFT). Its small size and fine-tuned nature suggest it is designed for efficient deployment in applications requiring specialized text generation or understanding, likely related to food extraction given its name.
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Model Overview
The dzakyalfitra/FoodExtract-gemma-3-270m-fine-tune-v1 is a specialized language model derived from the Google Gemma-3-270m-it architecture. This 0.3 billion parameter model has undergone supervised fine-tuning (SFT) using the TRL library, indicating its adaptation for particular downstream tasks.
Key Characteristics
- Base Model: Fine-tuned from
google/gemma-3-270m-it. - Training Framework: Utilizes the TRL (Transformers Reinforcement Learning) library for fine-tuning.
- Training Method: Specifically trained with Supervised Fine-Tuning (SFT).
- Parameter Count: A compact 0.3 billion parameters, making it suitable for resource-constrained environments or applications requiring efficient inference.
Potential Use Cases
Given its fine-tuned nature and the model name "FoodExtract," this model is likely optimized for:
- Information Extraction: Identifying and extracting specific entities or data points related to food from text.
- Food-related Text Analysis: Processing and generating text within the domain of food, recipes, dietary information, or culinary descriptions.
- Specialized Question Answering: Answering queries focused on food-related topics.
Technical Details
The model was developed using:
- TRL: 1.8.0
- Transformers: 5.13.1
- Pytorch: 2.13.0+cu132
- Datasets: 5.0.0
- Tokenizers: 0.22.2