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A PyTorch-based system for large-scale model parallel training, memory optimization, and heterogeneous acceleration.

An extensible open-source MLOps framework that lets teams design portable, reproducible pipelines decoupled from infra stacks.

The book provides a comprehensive yet accessible introduction to probabilistic modeling and inference, covering topics like graphical models, Bayesian methods, and approximate inference. It balances theory with practical examples, making complex probabilistic concepts understandable for newcomers and useful for practitioners. Its impact lies in shaping how students and researchers approach uncertainty in machine learning, offering a unifying probabilistic perspective that has influenced research, teaching, and real-world applications across fields such as AI, robotics, and data science.

This tutorial offers a detailed, line-by-line PyTorch implementation of the Transformer model introduced in "Attention Is All You Need." It elucidates the model's architecture—comprising encoder-decoder structures with multi-head self-attention and feed-forward layers—enhancing understanding through annotated code and explanations. This resource serves as both an educational tool and a practical guide for implementing and comprehending Transformer-based models.

This book offers a comprehensive introduction to algorithmic information theory: it defines plain and prefix Kolmogorov complexity, explains the incompressibility method, relates complexity to Shannon information, and develops tests of randomness culminating in Martin-Löf randomness and Chaitin’s Ω. It surveys links to computability theory, mutual information, algorithmic statistics, Hausdorff dimension, ergodic theory, and data compression, providing numerous exercises and historical notes. By unifying complexity and randomness, it supplies rigorous tools for measuring information content, proving combinatorial lower bounds, and formalizing the notion of random infinite sequences, thus shaping modern theoretical computer science.

A Yunshan Networks open-source observability stack that delivers zero-code eBPF-based tracing, metrics and continuous profiling for cloud-native & AI workloads.

An independent research lab exploring new mediums of thought and expanding the imaginative powers of the human species.

Experience unparalleled image generation capabilities with SDXL Turbo and Stable Diffusion XL. Our models use shorter prompts and generate descriptive images with enhanced composition and realistic aesthetics.

Open-source framework that provides composable building blocks to create, orchestrate and monitor LLM-powered applications and agents.

Hugging Face’s Rust + Python server for high-throughput, multi-GPU text generation.

Data framework that connects large-language models to private or enterprise data via indexing, retrieval and agent orchestration.

Memory layer that lets AI agents remember users and context across sessions.