Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

Codebase for Aria - an Open Multimodal Native MoE

Speech To Speech: an effort for an open-sourced and modular GPT4-o

The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use th…

From the Tensor to Stable Diffusion, a rough outline for a 1 week course.

Gradient descent is cool and all, but what if we could delete it?

a tiny vectorstore implementation built with numpy.

DSPy: The framework for programming—not prompting—language models

Tools for merging pretrained large language models.

A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

Multimodal Deep Learning architectures that are more robust to noisy and adversarial data.

Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.

Open-source AI cookbook

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

OCR, layout analysis, reading order, table recognition in 90+ languages

GPU programming related news and material links

Curate better data for LLMs

A collaborative web application (virtual office) presented as a 16-bit RPG video game

You like pytorch? You like micrograd? You love tinygrad! ❤️

This repo is the homebase of a community driven course on Computer Vision with Neural Networks. Feel free to join us on the Hugging Face discord: hf.co/join/discord

A starter kit to help you get started developing your own maps for WorkAdventure

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

Materials for the Hugging Face Diffusion Models Course

Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models l…

🤗 Evaluate: A library for easily evaluating machine learning models and datasets.

A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)

Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.

Robust recipes to align language models with human and AI preferences