Starred repositories
Home for "How To Scale Your Model", a short blog-style textbook about scaling LLMs on TPUs
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Modalities, a PyTorch-native framework for distributed and reproducible foundation model training.
Fault tolerance for PyTorch (HSDP, LocalSGD, DiLoCo, Streaming DiLoCo)
Meta Lingua: a lean, efficient, and easy-to-hack codebase to research LLMs.
A repository for research on medium sized language models.
A curated list of engineering blogs
Repository includes SDK and code examples. More info https://polar.com/en/developers
Place where folks can contribute to 🤗 community events
🤗 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.
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Hydra is a framework for elegantly configuring complex applications
Table Transformer (TATR) is a deep learning model for extracting tables from unstructured documents (PDFs and images). This is also the official repository for the PubTables-1M dataset and GriTS ev…
Source for remoteintech.company — a community-maintained directory of remote-friendly tech companies
Azure Object Detection Accelerator. A repo for quickly and easily setting up a sample object detection project with training, labelling, inference, testing and deployment. The repo uses a synthetic…
Code and data for the research paper "Towards Open Set Deep Networks" A Bendale, T Boult, CVPR 2016
A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition
This is an implementation of the DeepView framework that was presented in the paper Schulz, A., Hinder, F., & Hammer, B. (2020): https://www.ijcai.org/Proceedings/2020/319. Also available on Arxiv …
A computer algebra system written in pure Python
Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"