SupportSage-TinyLlama-RAG
Model Description
SupportSage-TinyLlama-RAG is a specialized Retrieval-Augmented Generation (RAG) system built to optimize customer support workflows. It leverages the TinyLlama-1.1B architecture to provide high-accuracy, context-aware responses based on internal documentation and ticket history.
Model Details
- Developed by: Komron Khayumov
- Model type: RAG-enhanced Large Language Model
- Language: English
- Base Model: TinyLlama-1.1B
- Frameworks: LangChain, ChromaDB, and Streamlit
Intended Use
This model is designed for enterprise-level customer service automation. It is intended to be used in conjunction with a vector database (ChromaDB) to retrieve relevant context before generating answers, significantly reducing model hallucinations.
Training Data & Methodology
- Data Source: Tested and validated on 61,700 real-world customer support tickets.
- Workflow: The system uses semantic search to fetch relevant tickets from a ChromaDB vector store before passing the context to the LLM.
Performance Metrics
- System Accuracy: 77.11%
- Inference Speed: Highly optimized for real-time customer interactions using a small-footprint 1.1B parameter model.
How to Get Started
You can access the live demo of this model integrated into its UI here: SupportSage AI Space