Model Training
Showing 36 of 74 projects
A model-definition framework for state-of-the-art machine learning models across text, vision, audio, and multimodal tasks.
A model-definition framework for state-of-the-art machine learning models across text, vision, audio, and multimodal tasks.
A multi-backend deep learning framework that enables effortless model development across JAX, TensorFlow, PyTorch, and OpenVINO.
A cutting-edge framework for training and deploying state-of-the-art YOLO models for object detection, segmentation, classification, and pose estimation.
An open platform for training, serving, and evaluating large language model based chatbots.
A modular PyTorch library for state-of-the-art diffusion models to generate images, audio, and 3D molecular structures.
A PyTorch wrapper that automates engineering boilerplate for scalable AI model training and deployment.
A deep learning framework to pretrain and finetune any AI model at any scale with zero code changes.
A deep learning framework to pretrain and finetune any AI model on any hardware with zero code changes.
An industrial deep learning framework supporting unified dynamic/static graphs, automatic parallelism, and integrated training/inference for large models.
An industrial deep learning framework from China supporting unified dynamic/static graphs, automatic parallelism, and integrated training/inference for large models.
A flexible and efficient deep learning framework that mixes symbolic and imperative programming for heterogeneous distributed systems.
A hardware-accelerated JavaScript library for training and deploying machine learning models in the browser and Node.js.
A hardware-accelerated JavaScript library for training and deploying machine learning models in browsers and Node.js.
A Rust-based deep learning framework and tensor library optimized for flexibility, efficiency, and cross-platform portability.
A low-code declarative framework for building custom LLMs, neural networks, and other AI models with YAML configurations.
A Python library for building custom machine learning models for tasks like image classification, object detection, and recommendations.
An open-source, cross-platform machine learning framework for .NET developers to build, train, and deploy custom ML models.
An end-to-end deep learning library focused on clear code and speed, used for research and production by Google Brain.
An end-to-end deep learning library focused on clear code, speed, and research, built by Google Brain.
A PyTorch-based open-source framework for deep learning in healthcare imaging, providing domain-specific tools and workflows.
A TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers with customizable neural layers.
A neural network library for JAX designed for flexibility, enabling researchers to experiment with new training forms by modifying training loops.
A neural network library for JAX designed for flexibility, enabling researchers to experiment with new training forms by modifying training loops.
A curated collection of standalone examples demonstrating machine learning tasks with TensorFlow.js in browsers and Node.js.
An open-source, self-hosted ML experiment tracker with a performant UI and SDK for comparing and querying training runs.
An archived experiment integrating TensorFlow's machine learning capabilities directly into the Swift programming language with first-class differentiable programming.
An open-source pipeline for training medical domain GPT models using PT, SFT, RLHF, DPO, ORPO, and GRPO methods.
An engine-agnostic deep learning framework for Java developers, providing a high-level API for model training and inference.
A collection of samples demonstrating how to use ML.NET for various machine learning tasks in .NET applications.
A web application for training deep learning models with a focus on computer vision tasks.
Enables distributed TensorFlow training and inferencing on Apache Spark and Hadoop clusters with minimal code changes.
A JAX library for rapid prototyping of large-scale attention-based vision models across images, video, audio, and multimodal data.
A TensorFlow project template with a well-designed folder structure and OOP design to accelerate deep learning development.
A PyTorch-based framework for visual object tracking and video object segmentation, featuring implementations of state-of-the-art trackers like TaMOs, RTS, and DiMP.
A PyTorch framework for deep learning research and development, focusing on reproducibility and rapid experimentation.
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