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Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

关于domain generalization，domain adaptation，causality，robutness，prompt，optimization，generative model各式各样研究的阅读笔记

Pruna is a model optimization framework built for developers, enabling you to deliver faster, more efficient models with minimal overhead.

Qualcomm® AI Hub Models is our collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm® devices.

Cross-Platform Production-ready C++ inference engine for YOLO models (v5-v12, YOLO26). Unified API for detection, segmentation, pose estimation, OBB, and classification. Built on ONNX Runtime and OpenCV. Optimized for CPU/GPU with quantization support.

The official implementation of “Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training”

An Automatic Prompt Optimization Framework for Large Language Models

A scalable inference server for models optimized with OpenVINO™

Optimized Whisper models for streaming and on-device use