Hugging Face DataGemma RAG: Bridging LLMs and Statistical Data
DataGemma RAG is a 27 billion parameter model, fine-tuned from the Gemma 2 series by Google, specifically engineered to facilitate the integration of reliable public statistical data from Data Commons into LLM responses. Its primary function within a Retrieval Augmented Generation (RAG) framework is to translate user queries into natural language statistical questions that Data Commons' existing interface can process.
Key Capabilities
- Statistical Question Generation: Takes a user query and generates a list of statistical questions in predefined formats (e.g., "What is $METRIC in $PLACE?").
- Data Commons Integration: Designed to work seamlessly with Data Commons' natural language interface, enabling LLMs to query and retrieve statistical information.
- Fine-tuned for RAG: Optimized for RAG workflows, where it acts as a crucial component for data retrieval.
Usage and Limitations
This model is an early version, intended primarily for academic and research purposes, not for commercial or general public use. It was trained on a small corpus of synthetic data and may exhibit limitations or unintended behaviors. Users should anticipate errors and consult the DataGemma paper for a comprehensive understanding of its capabilities and known limitations. The model's performance is evaluated as part of the full RAG workflow described in the paper.