VILA-Lab/DRAG
(ACL 2025 Main) Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation
This project helps AI developers create smaller, more efficient Retrieval-Augmented Generation (RAG) models. It takes knowledge and reasoning abilities from large, powerful language models (LLMs) and transfers them into smaller language models (SLMs). The output is an SLM that can answer factual questions more accurately and with less "hallucinated" content, while significantly reducing computational costs and model size.
No commits in the last 6 months.
Use this if you need to deploy a RAG system that is factually consistent and computationally lightweight, without sacrificing accuracy.
Not ideal if you are a business user looking for a ready-to-use RAG application, as this is a framework for AI model development.
Stars
34
Forks
2
Language
Python
License
—
Category
Last pushed
Aug 23, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/VILA-Lab/DRAG"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
LearningCircuit/local-deep-research
Local Deep Research achieves ~95% on SimpleQA benchmark (tested with GPT-4.1-mini). Supports...
NVIDIA-AI-Blueprints/rag
This NVIDIA RAG blueprint serves as a reference solution for a foundational Retrieval Augmented...
Denis2054/RAG-Driven-Generative-AI
This repository provides programs to build Retrieval Augmented Generation (RAG) code for...
hienhayho/rag-colls
Collection of recent advanced RAG techniques.
jeremiahbohr/literature-mapper
Transform academic PDFs into a Knowledge Graph with typed claims, temporal analysis,...