Awesome GitHub RepositoriesDeployment & Serving

MinerU is a document parsing pipeline designed to transform unstructured files into machine-readable, structured data. It utilizes deep learning models to perform layout analysis, identifying document regions and extracting complex content such as mathematical expressions. By combining these neural network inferences w

Deploys deep learning models to classify content types and extract complex mathematical expressions from diverse visual inputs.

Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification

Optimizes model weights and architectures for efficient inference on low-power embedded hardware.

This project provides a deep learning architecture designed to identify and isolate distinct objects within images by generating precise pixel-level masks. It functions as a browser-based inference engine, enabling the execution of complex machine learning models directly within web environments without requiring serve

Exports complex models into standardized formats to enable robust, multithreaded inference within web applications.

Unsloth is a high-performance training and inference platform designed to optimize the lifecycle of large language and multimodal models. It provides a comprehensive engine for fine-tuning, executing, and managing models locally, with a focus on reducing memory consumption and increasing compute speed on consumer-grade

Exported model weights upload directly to central repositories for hosting and sharing using standard authentication tokens.

This repository provides a collection of practical demonstrations and implementation guides for machine learning tasks using TensorFlow.js. It serves as a resource for developers to explore model architectures, training workflows, and data manipulation techniques across domains such as computer vision, natural language

Low-level interfaces allow for precise weight initialization and the construction of custom model architectures using granular tensor operations.