paper2summary
A resource-efficient RAG engine for scientific paper question answering
Quick Summary
- Developed a RAG engine for scientific paper QA with accurate source citations
- Enhanced Llama-3.2-1B for paper summarization via LoRA fine-tuning (7% trainable parameters), achieving 51% higher ROUGE-2 and 13% ROUGE-L scores
- Deployed a hybrid text/vector retrieval system featuring LLM reranking and citation visualization on laptop hardware
Tools: PEFT, Transformers, Weights & Biases, Kotaemon, ChromaDB
Video Demo
For more detailed project information, please visit the GitHub repo: https://github.com/gabe-zhang/paper2summary