Model Training
Showing 36 of 45 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 deep learning framework to pretrain and finetune any AI model on any hardware with zero code changes.
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.
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 archived experiment integrating TensorFlow's machine learning capabilities directly into the Swift programming language with first-class differentiable programming.
An open-source, self-hosted ML experiment tracker with a performant UI and SDK for comparing and querying training runs.
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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