Official Training and Benchmarking Implementation of PDMD: Projected Distribution Matching Distillation for Video Diffusion Models
BarunLM-35M: a parameter-efficient 35M base language model trained on 5.7B tokens
TTPO: Test-Time Policy Optimization
Post training code for IFM K2 Horizon
Training DroidUp E1 humanoid robot locomotion using MJLab with AMP (Adversarial Motion Prior) and Mimic learning in simulation.
Fewer Tokens, Better Action: GPT-6 Astra Robot Agents with 14% Higher Success but 65% Fewer Tokens
μ½λ μΌ+(ꡬ μ½λ μΌν‘) μ±κ³Ό κ°μ μμ²μ 보λ΄λ λΉκ³΅μ Python ν΄λΌμ΄μΈνΈ
Safety-critical longitudinal planning: from DP+QP+PID to DP+augmented-state MPC with safety constraint formulation with RSS model and iLQR solver.
[NeurIPS 2026] Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning
π KTX ν, νμ΄μ¬μ΄ λμ΅λλ€ β μ΄μ°¨ μ‘°ν Β· μλ§€ Β· κ²°μ Β· νλΆ Β· μμ½λκΈ° Β· μ·¨μν κ°μλ₯Ό μ½λ λͺ μ€λ‘. μ½λ μΌν‘ API λΉκ³΅μ Python SDK (Unofficial Korail / KTX API client)
P2T: Unlocking Token Rewards via Training-Free Reward Attribution (CVPR 2026) - official code
A fruit fly connectome (165k neurons, 25.6M synapses) trained to drive a simulated street from pixels. Code, patches, renderer, results.
GraspGen-based dual-arm manipulation with X-Trainer and Isaac Sim, covering grasping, transport, alignment, insertion and release.
Turn authorized motion references into a consistent AI digital-human sports coach, reusable action library, timed course, and voice-guided training experience.
[ACM MM 2026] PURE: Refining Feed-forward 3D Reconstruction with Unlabeled Post-training
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
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.
Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls)
PaddleFormers is an easy-to-use library of pre-trained large language model zoo based on PaddlePaddle.
Low-code framework for building custom LLMs, neural networks, and other AI models
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.
Build, Manage and Deploy AI/ML Systems
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch
Democratizing Reinforcement Learning for LLMs
A treasure chest for visual classification and recognition powered by PaddlePaddle
A PyTorch native platform for training generative AI models
A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)
A hardware-agnostic SDK for optimization problems: define a problem once, then lower it to any quantum or classical solver
A Next-Generation Training Engine Built for Ultra-Large MoE Models
The open source research environment for AI researchers to seamlessly train, evaluate, and scale models from local hardware to GPU clusters.
A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
π€ AutoTrain Advanced
Pre-trained Deep Learning models and demos (high quality and extremely fast)
Post-training with Tinker
AllenAI's post-training codebase
π Accelerate inference and training of π€ Transformers, Diffusers, TIMM and Sentence Transformers with easy to use hardware optimization tools
A high-performance distributed training framework for Reinforcement Learning
Constraint-based geometry sketcher for blender
LLM-as-a-Verifier is a general-purpose framework that provides fine-grained feedback for any agent without requiring additional training. It achieves SOTA performance across coding, robotics, and medical agentic benchmarks.
OneTrainer is a one-stop solution for all your Diffusion training needs.
PyTorch native quantization for training and inference
Minimalistic large language model 3D-parallelism training
The code used to train and run inference with the ColVision models, e.g. ColPali, ColQwen2, and ColSmol.
Images to inference with no labeling (use foundation models to train supervised models).
SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; leading model compression techniques on PyTorch, TensorFlow, and ONNX Runtime
The PyTorch-based audio source separation toolkit for researchers
Improve your Baduk skills by training with KataGo!
A python Linear Programming API
ProtoMotions is a GPU-accelerated simulation and learning framework for training physically simulated digital humans and humanoid robots.
The collection of pre-trained, state-of-the-art AI models for ailia SDK
A lightweight suite of motion imitation methods for training controllers.
A unified orchestration layer for heterogeneous AI compute. It standardizes how to manage compute and run training and inference on GPU clouds, Kubernetes, VMs, or bare-metal clusters.
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project provides a series of 3D-ResNet pre-trained models and relative code.
A library for training and deploying machine learning models on Amazon SageMaker
VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
Code, labs, and resources for O'Reilly AI Systems Performance Engineering: GPU optimization, distributed training, inference scaling, and full-stack tuning.
Library for training machine learning models with privacy for training data