Stars
Lightweight coding agent that runs in your terminal
The agent that grows with you
Train the smallest LM you can that fits in 16MB. Best model wins!
Anthropic's original performance take-home, now open for you to try!
Машинное обучение на ФКН ВШЭ
Accelerating MoE with IO and Tile-aware Optimizations
Machine Learning Engineering Open Book
Accessible large language models via k-bit quantization for PyTorch.
Efficient Triton Kernels for LLM Training
GPU programming related news and material links
Tensors and Dynamic neural networks in Python with strong GPU acceleration
PyTorch native quantization and sparsity for training and inference
A PyTorch native platform for training generative AI models
FlashMLA: Efficient Multi-head Latent Attention Kernels
DeepEP: an efficient expert-parallel communication library
DeepGEMM: clean and efficient BLAS kernel library on GPU
Fast and memory-efficient exact attention
Ongoing research training transformer models at scale
🚀 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
🤗 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.
Это репозиторий для проведения курса по The Missing Semester of Your CS Education в университете Высшая Школа Экономики
Deep Generative Models Course
Course about deep learning for computer vision and graphics co-developed by YSDA and Skoltech.
YSDA course in Speech Processing.
A course in reinforcement learning in the wild
YSDA course in Natural Language Processing
Efficient Deep Learning Systems course materials (HSE, YSDA)