glaiveai/Llama-3-8B-RAG-v1
glaiveai/Llama-3-8B-RAG-v1 is an 8 billion parameter Llama-3 based model developed by Glaive, fine-tuned specifically for Retrieval-Augmented Generation (RAG) tasks. It excels at generating grounded answers from provided documents and supports both grounded and mixed answer modes with integrated citation generation. The model is optimized for accurate information retrieval and synthesis from given contexts, making it suitable for question-answering systems requiring verifiable sources.
Loading preview...
Overview
glaiveai/Llama-3-8B-RAG-v1 is an 8 billion parameter model developed by Glaive, built upon the Llama-3 architecture. It has been specifically fine-tuned for Retrieval-Augmented Generation (RAG) tasks using the Glaive-RAG-v1 dataset. This model is designed to provide answers based on provided documents, with a strong emphasis on factual grounding and citation.
Key Capabilities
- Retrieval-Augmented Generation (RAG): Optimized to generate responses by leveraging information from external documents.
- Answer Modes: Supports two distinct answer modes:
- Grounded: Responds strictly with facts extracted from the provided documents.
- Mixed: Combines facts from documents with the model's internal knowledge.
- Citation Generation: Automatically generates citations, both at the beginning of the answer and inline within the text using
<co: doc_id></co>tags, linking information directly to its source document. - Structured Input: Designed to work with a specific input format, including document content, titles, and user queries, to ensure optimal performance.
Use Cases
This model is particularly well-suited for applications requiring accurate, verifiable, and source-attributable responses. Ideal scenarios include:
- Grounded Question Answering: Answering user queries based on a provided corpus of documents.
- Information Extraction: Extracting specific facts and details from texts with clear source attribution.
- Knowledge Base Interaction: Building conversational agents that can reference and cite information from a knowledge base.
Developers should adhere to the specified system prompt and document formatting to achieve the best results, as this aligns with the model's fine-tuning methodology.