alfiannajih/g-retriever-resume-reviewer

Model Card for Model ID

This repository is created for submission to Compfest: Artificial Intelligence Challenge (AIC) 16.

G-Retriever integrates Graph Neural Networks (GNN), Large Language Model (LLM), and Retrieval-Augmented Generation(RAG) by using Knowledge Graph. This model was originaly developed by Xiaoxin He.

Model Details

Model Description

While the original method utilized Llama 2 family model as the LLM, this repository has experimented it with Llama 3.1 8B.

Model Sources

• Repository: Repository
• Training Script: G-Retriever Repository
• Paper: G-Retriever Paper

Uses

This model is designed to be used as a resume reviewer. The approach involves retrieving a subgraph from a knowledge graph built from LinkedIn job postings and feeding it into a GNN. The features extracted from the subgraph are further processed and concatenated with the input embeddings from the query text. These concatenated features are then passed through the self-attention layer of Llama 3.1 8B to generate a resume review.