🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
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Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Making large AI models cheaper, faster and more accessible
RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.
Low-code framework for building custom LLMs, neural networks, and other AI models
The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer, so frontier AI teams build custom intelligence faster.
PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
OpenMMLab Foundational Library for Training Deep Learning Models
Official Repository for the Uni-Mol Series Methods
allRank is a framework for training learning-to-rank neural models based on PyTorch.
RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation