20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
👀 Train a 65M-parameter VLM from scratch in just 2h!
Data processing for and with foundation models! 🍎 🍋 🌽 ➡️ ➡️🍸 🍹 🍷
The official CLI and Python client for the Hugging Face Hub.
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit and 4-bit floating point (FP8 and FP4) precision on Hopper, Ada and Blackwell GPUs, to provide better performance with lower memory utilization in both training and inference.
One-ink editorial print image skill — warm paper, halftone photography, active negative space, and restrained typography.
Code for the paper "Evaluating Large Language Models Trained on Code"
Miles is an enterprise-facing reinforcement learning framework for LLM and VLM post-training, forked from and co-evolving with slime.
RL training environments for Microduck (mjlab)
Agentic RL Training at Scale
High quality training free inpaint for every stable diffusion model. Supports ComfyUI
Multimodal RL training framework for diffusion & omni models
Training library for Megatron-based models with bidirectional Hugging Face conversion capability
Halo is an open-source framework built by White Circle for training large language and multimodal models
Finathink — Financial Research Optimizer for auditable, point-in-time research, mathematical learning, and constrained portfolio analysis.
Feather agent harness, training integrations, and evaluation tools for knowledge work
Offline Clash Royale AI agent research: native simulation, imitation learning, PPO, and interactive evaluation. 离线对局模拟、模仿学习、强化学习与交互评测。
A framework for building robust RL environments to train aligned AI.
Official implementation of "Correction Training for Diffusion Models"
Making large AI models cheaper, faster and more accessible
Machine Learning Engineering Open Book
Ongoing research training transformer models at scale
An open source implementation of CLIP.
Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, GPT-OSS, Llama, and more!
The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer, so frontier AI teams build custom intelligence faster.
🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
Tools for merging pretrained large language models.
SD-Trainer. LoRA & Dreambooth training scripts & GUI use kohya-ss's trainer, for diffusion model.
A system for quickly generating training data with weak supervision
Train the smallest LM you can that fits in 16MB. Best model wins!
H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://docs.h2o.ai/h2o-llmstudio/
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
⚡️SwanLab - an open-source, modern-design AI training tracking and visualization tool. Supports Cloud / Self-hosted use. Integrated with PyTorch / Transformers / verl / LLaMA Factory / ms-swift / Ultralytics / MMEngine / Keras etc.
TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data
Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
A Lightweight LLM Post-Training Library
HRM-Text is a 1B text generation model based on the HRM architecture, strengthened by task completion and latent space reasoning.
[CVPR 2026] Towards Real-Time Diffusion-Based Streaming Video Super-Resolution — An efficient one-step diffusion framework for streaming VSR with locality-constrained sparse attention and a tiny conditional decoder.
Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning
Training Sparse Autoencoders on Language Models
OpenOCR: An Open-Source Toolkit for General-OCR Research and Applications, integrates a unified training and evaluation benchmark, commercial-grade OCR and Document Parsing systems, and faithful reproductions of the core implementations from a wide range of academic papers.
PyTorch compiler that accelerates training and inference. Get built-in optimizations for performance, memory, parallelism, and easily write your own.
Easy to use stem (e.g. instrumental/vocals) separation from CLI or as a python package, using a variety of amazing pre-trained models (primarily from UVR)
Toolkit to segment text into sentences or other semantic units in a robust, efficient and adaptable way.
Search, understand, reproduce, and improve an idea with ease
MAC (Multi-Agent CAD): A decoupled multi-agent framework for text-to-CAD generation via constrained test-time compute
🚀 Pytorch Distributed native training library for LLMs/VLMs with OOTB Hugging Face support
[EMNLP 2026] MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research · 浏览器里运行的安卓模拟器 · Browser-hosted Android Simulator · Verifiable Evaluation · Scalable Online RL Training
Turn any repository into verifiable RL environments for coding agents - Harbor tasks you can train on, evaluate and share on the Hugging Face Hub
COBRApy is a package for constraint-based modeling of metabolic networks.
54 rigorous skills for Codex, OpenCode, and Pi: code review, security audit, feature development, frontend design, MCP tools, Hugging Face ML/training, and more.
Pretraining Transformers Without Backpropagation
[NeurIPS 2026] Beyond Data Scaling: Representation-Centric Continued Pre-training for Vision-Language-Action Models
Train RL policies for the Pollen Microduck 🦆 on an ordinary Mac, no CUDA GPU, and watch them learn live in the browser.
Light-weight infrastructure for express LLM pre-training
Open infrastructure for training, evaluating, and deploying System 1 decision models across language and multimodal backbones.
Bonsai 2 27B: 262k context (trained max) on 12 GB, 54 to 97 tok/s decode, 2x prefill, q8_0 KV recipe, grammar tool calls 9/9. Same ternary weights. Windows bundle + receipts.