Llama-3-8B-Instruct-Finance-RAG-GGUF
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| Model Type | Instruction-tuned Language Model |
| License | LLaMA 3 |
| Base Model | meta-llama/Meta-Llama-3-8B-Instruct |
| Training Dataset | virattt/financial-qa-10K |
What is Llama-3-8B-Instruct-Finance-RAG-GGUF?
This is a specialized quantized version of the Llama 3 model, specifically fine-tuned for financial question-answering tasks using RAG (Retrieval-Augmented Generation) applications. The model has been optimized using llama.cpp for efficient deployment and uses the GGUF format for improved compatibility and performance.
Implementation Details
The model is built upon the Llama 3 8B Instruct architecture and has been fine-tuned using a LoRA adapter on 4,000 examples from the financial-qa-10K dataset. It's specifically designed to process questions based on provided context, making it ideal for RAG applications in the financial domain.
- Utilizes the efficient GGUF quantization format
- Implements context-aware question answering
- Optimized for financial domain queries
- Built with transformers library support
Core Capabilities
- Financial question-answering based on provided context
- Processing and understanding of financial statements and reports
- Structured response generation for financial queries
- Support for both direct questions and context-based inquiries
Frequently Asked Questions
Q: What makes this model unique?
This model combines the power of Llama 3 with specialized financial domain knowledge, optimized through GGUF quantization for efficient deployment. Its RAG-specific training makes it particularly effective for financial question-answering scenarios where context needs to be considered.
Q: What are the recommended use cases?
The model is ideal for applications involving financial document analysis, automated financial Q&A systems, and financial research assistance. It's particularly suited for scenarios where questions need to be answered based on specific financial contexts or documents.
