ReHGNN is a deep learning framework for intelligent server recommendation in distributed large-model deployment scenarios.
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Updated
Nov 26, 2025 - Python
ReHGNN is a deep learning framework for intelligent server recommendation in distributed large-model deployment scenarios.
A robust, modular, and production-ready platform for solar power system data analysis, machine learning, and prediction. Built with ZenML, Streamlit, MLflow, and a rich Python data science stack, this project enables end-to-end workflows from data ingestion and EDA to model training, deployment, inference and experiment tracking
End-to-end offline OCR and semantic parsing pipeline for identity documents based on YOLOv11, PaddleOCR, and LLaMA-3.1.
The MultiGen pathfinding model aims to predict a suitable model invocation path based on user input requirements, using known model information.
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