training-framework

Simple and efficient training framework for long-context models

Repo of CACL framework for bot detection

Serious calibration and benchmarking for the `state_collapser` HRL package

A PyTorch framework that handles object detection across 6 different architectures (RetinaNet, Faster R-CNN, SSD, FCOS, and more). Takes care of the optimization setup and training quirks for each model.

Intelligent training framework that automatically skips mastered samples and gives 5× more compute to hard ones. Up to 80% compute savings on LLM fine-tuning.

A Toy Framework for Model Training

Zero-RAM, JAX-Centric Dataloading, Streaming, and Asynchronous Checkpointing Toolkit

Fault-tolerant distributed training framework with async checkpointing for LLM's

A comprehensive framework for developing YOLO family models, featuring streamlined workflows for training, validation, testing, and deployment through easy-to-use config files, enabling flexible customization to suit various object detection tasks.

A PyTorch framework for image classification covering 11 CNN architectures (ResNet, EfficientNet, MobileNet, etc.). Handles the optimization setup and training specifics for each model.

Library for config based Neural Network Training

PyTorch training framework with AMP, checkpointing, TensorBoard, profiling, and modular experiment pipelines.