Awesome GitHub RepositoriesHugging Face

Lens is a multi-cluster management platform and desktop application for administering Kubernetes environments. It provides a graphical interface for deploying Helm charts, editing YAML manifests, and managing the lifecycle of pods and deployments. The project features an AI-powered cluster assistant that enables users to query cluster state, perform autonomous troubleshooting, and translate natural language requests into system commands. It also supports collaborative team access through shared spaces, utilizing encrypted cluster sharing and role-based access control to manage credentials and

OpenVINO is an AI inference engine and model serving platform designed to execute optimized deep learning models across CPUs, GPUs, and NPUs through a unified API. It includes a model optimization toolkit for converting, quantizing, and compressing models from various frameworks, alongside a specialized generative AI runtime for large language models. The project distinguishes itself through a plugin-based hardware acceleration layer that maps neural network operations to vendor-specific drivers. It features advanced execution mechanisms such as continuous batching, speculative decoding, and

Snapshot is a decentralized governance platform that enables DAOs and organizations to create and manage onchain voting systems. It supports both offchain signing and onchain execution, allowing votes to be submitted as signed messages to reduce gas costs while maintaining verifiability through onchain proposal execution on Ethereum and Starknet. The platform provides a modular strategy plugin system for voting power calculation and proposal validation, along with hierarchical space and role management for multi-tier governance structures. Snapshot distinguishes itself through several advance

Depth-Anything is a monocular depth estimation foundation model that produces dense per-pixel depth maps from a single RGB image. It is built on a DINOv2 Vision Transformer encoder backbone and trained on 62 million unlabeled images using a teacher-student pseudo-labeling framework, enabling robust generalization across diverse scenes without task-specific training. The model outputs both relative depth maps, which capture the ordering of scene points, and metric depth maps with real-world units after fine-tuning on datasets like NYUv2 or KITTI. The project distinguishes itself through its ab

Microsandbox is a runtime for creating and managing lightweight, hardware-isolated virtual machines — called sandboxes — that boot directly from standard OCI container images. Each sandbox runs as its own host process with a separate kernel, filesystem, and network stack, providing process-per-sandbox isolation. The project includes a command-line tool and multi-language SDKs (Rust, TypeScript, Python, Go) for programmatic lifecycle control, and it communicates with sandbox agents over Unix sockets using a CBOR-encoded protocol. What distinguishes Microsandbox is its combination of host-manag

mistral.rs is an inference engine for large language models that runs locally and exposes models behind OpenAI and Anthropic-compatible APIs. It serves as a multi-model serving platform, capable of loading several models in a single server process with per-request routing and on-demand loading and unloading. The engine supports multimodal inference, processing text alongside images, video, audio, and speech inputs, and includes a quantized model deployment runtime that reduces memory use and speeds up inference on consumer hardware. The project distinguishes itself through an agentic tool exe

DocTR is a deep learning OCR library built on PyTorch that detects and transcribes text in document images using a two-stage detection-recognition pipeline. It provides a complete framework for building and deploying OCR pipelines with pretrained models available through the Hugging Face Hub, and supports exporting trained models to ONNX format for cross-runtime deployment. The library offers end-to-end OCR pipelines that combine text detection and recognition to extract all text from document images or PDFs, with support for rotated page handling and varied text orientations. It includes cap

Liger-Kernel is a collection of pre-built fused Triton kernels and patching utilities designed to accelerate large language model training. It provides drop-in kernel replacements for common LLM operations such as RMSNorm, cross-entropy loss, and attention, enabling increased throughput and reduced memory usage while preserving bitwise-exact gradients. The project serves as a toolkit for composing custom model architectures from individual optimized kernels and for patching pre-existing models with minimal code changes. The project distinguishes itself through its ability to perform runtime m

Torchtune is a PyTorch-native library for fine-tuning, aligning, and quantizing large language models. It provides a config-driven system for instantiating components, orchestrating distributed training, and managing parameter-efficient fine-tuning with quantization support, all through YAML-based configurations and command-line overrides. The library distinguishes itself through its comprehensive post-training workflow orchestration, combining supervised fine-tuning, preference optimization (DPO, PPO, GRPO), knowledge distillation, and quantization-aware training in a single configurable pip

Torchtune is a PyTorch-native library for fine-tuning, aligning, and quantizing large language models. It provides a configurable training pipeline orchestrated through YAML recipes, with CLI overrides and component swapping, distributed training via FSDP2, memory optimizations, and parameter-efficient fine-tuning methods like LoRA, DoRA, and QLoRA. The library distinguishes itself through its YAML-driven configuration system that defines all training parameters and instantiates components from config files, with full CLI override capability for any field or component at launch time. It suppo

KServe is an open platform for deploying and serving generative and predictive AI models on Kubernetes. It defines inference services as custom resources with declarative YAML specifications, enabling a Kubernetes-native approach to model deployment and lifecycle management. The platform leverages Knative-based serverless scaling for automatic scale-to-zero and revision management, and supports a pluggable serving runtime architecture that maps model formats to containerized execution environments. KServe distinguishes itself through model-aware autoscaling that scales replicas based on token

KServe is a Kubernetes-native platform for deploying and serving machine learning models as scalable inference services. It supports both generative AI models, including large language models, and traditional predictive models from frameworks such as TensorFlow, PyTorch, Scikit-Learn, XGBoost, and ONNX. The platform manages the full lifecycle of model deployments, including revision tracking, canary rollouts, A/B testing, and automatic rollbacks, and provides serverless scale-to-zero capabilities for cost-efficient resource management. KServe distinguishes itself through a standardized infere

LXD is a unified platform for managing both system containers and virtual machines through a single REST API and command-line interface. It provides a programmatic HTTP interface for controlling the full lifecycle of instances, enabling automation and integration with external tools. The system runs unprivileged containers with per-instance UID/GID mappings, seccomp filters, and AppArmor profiles for kernel-level isolation, while supporting multiple storage backends including directory, Btrfs, LVM, ZFS, Ceph, LINSTOR, and TrueNAS through a unified driver interface. The platform distinguishes

This project is a collection of transformer natural language processing tutorial notebooks and educational resources. It provides a guide for using the Hugging Face Transformers library through interactive coding exercises and demonstrations. The repository contains ready-to-run Jupyter notebooks that provide practical examples for implementing transformer models. These resources demonstrate how to execute specific natural language processing workflows using pre-trained models. The notebooks cover a range of natural language processing tasks, including text classification, automatic text sum

This is a collection of Jupyter notebooks that serve as educational guides for training, fine-tuning, and deploying machine learning models within the Hugging Face ecosystem. The notebooks cover the full lifecycle of model development, from loading and configuring pre-trained transformers to packaging trained models for real-time inference via scalable endpoints. The notebooks demonstrate a range of capabilities including diffusion model training and fine-tuning for image generation and editing, transformer model adaptation for natural language processing tasks, and parameter-efficient fine-t

This project is an educational course and collection of training materials focused on generative diffusion models. It provides a curriculum and practical guides for training, fine-tuning, and deploying models capable of synthesizing images, audio, and video. The material covers specific implementation strategies including noise-based synthesis, iterative refinement, and latent space compression. It provides instruction on guiding generative outputs through conditional synthesis and prompt adherence optimization, as well as techniques for image inpainting and text-based editing. The project i

SWE-bench is an automated evaluation framework that tests large language models on real-world software engineering tasks. It measures how effectively models can generate and apply code patches that resolve actual GitHub issues, using a standardized dataset and scoring system built around Docker-based patch verification against original project test suites. The framework provides curated benchmark datasets spanning comprehensive, fast, verified, multilingual, and multimodal evaluation splits, allowing targeted assessment of model capabilities across different programming languages and issue ty

SLIME is a distributed reinforcement learning framework for large language model post-training that bridges Megatron training with SGLang inference servers. It orchestrates scalable RL loops across GPU clusters, decoupling training and inference into independent processes that communicate over HTTP and NCCL for independent scaling and fault tolerance. The system supports multi-agent reinforcement learning workflows with parallel agent instances, customizable rollout strategies, and personalized agent serving that improves models from prior conversations without disrupting API serving. The fra