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--- |
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library_name: transformers |
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tags: |
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- knowledge graph |
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- rag |
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- gnn |
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base_model: NousResearch/Hermes-3-Llama-3.1-8B |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This repository is created for submission to Compfest: Artificial Intelligence Challenge (AIC) 16. |
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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. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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While the original method utilized Llama 2 family model as the LLM, this repository has experimented it with Llama 3.1 8B. |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [Repository](https://github.com/alfiannajih/job-recommender) |
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- **Training Script:** [G-Retriever Repository](https://github.com/XiaoxinHe/G-Retriever) |
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- **Paper:** [G-Retriever Paper](https://arxiv.org/abs/2402.07630) |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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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. |
