Simple and efficient training framework for long-context models
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Updated
Jan 12, 2026 - Python
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 comprehensive framework for fine-tuning OpenAI models with streamlined data preparation, training, and evaluation workflows
Fault-tolerant distributed training framework with async checkpointing for LLM's
A PyTorch framework for image classification covering 11 CNN architectures (ResNet, EfficientNet, MobileNet, etc.). Handles the optimization setup and training specifics for each model.
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.
Library for config based Neural Network Training
Zero-RAM, JAX-Centric Dataloading, Streaming, and Asynchronous Checkpointing Toolkit
PyTorch training framework with AMP, checkpointing, TensorBoard, profiling, and modular experiment pipelines.
A Toy Framework for Model Training
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