Awesome GitHub RepositoriesPost-Training Quantization

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

This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene

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

AutoGPTQ एक मॉडल कम्प्रेशन टूलकिट और पोस्ट-ट्रेनिंग क्वांटाइजेशन फ्रेमवर्क है जिसे लार्ज लैंग्वेज मॉडल्स के मेमोरी फुटप्रिंट को कम करने के लिए डिज़ाइन किया गया है। यह न्यूरल नेटवर्क वेट्स को कंप्रेस करने के लिए GPTQ एल्गोरिदम का उपयोग करता है, जिससे हार्डवेयर आवश्यकताएं कम होती हैं और VRAM उपयोग कम होता है। यह प्रोजेक्ट एक इन्फरेंस एक्सेलेरेटर के रूप में कार्य करता है जो ऑप्टिमाइज़्ड कर्नेल प्रदान करता है जो टोकन जनरेशन की गति को बढ़ाता है। इसमें मॉडल आर्किटेक्चर एक्स्टेंसिबिलिटी की सुविधा है, जो कॉन्फ़िगर करने योग्य पैटर्न्स के माध्यम से नई मॉडल संरचनाओं में क्वांटाइजेशन क्षमताओं को जोड़ने की अनुमति देती है। यह फ्रेमवर्क एक व्यापक क्वांटाइजेशन पाइपलाइन को कवर करता है, जिसमें लेयर-वार वेट कम्प्रेशन, कैलिब्रेशन-आधारित स्केल अनुमान और प्रिसिजन-विशिष्ट मेमोरी मैपिंग शामिल है। इसमें भाषा और सारांश कार्यों में सटीकता पर क्वांटाइजेशन के प्रभाव को मापने के लिए मॉडल परफॉरमेंस मूल्यांकन के लिए सिस्टम भी शामिल हैं।

This project is a comprehensive instructional resource and course for building neural networks using PyTorch. It covers the fundamental building blocks of deep learning, including tensor manipulation, automatic differentiation, and the construction of modular neural network components. The repository serves as a technical guide for several specialized domains. It provides implementation details for computer vision tasks such as image classification, object detection, and semantic segmentation, as well as natural language processing workflows involving transformers, recurrent networks, and gen

ExecuTorch is a lightweight C++ runtime for deploying PyTorch models on mobile, embedded, and edge hardware. It provides an ahead-of-time compilation pipeline that exports, quantizes, and lowers model graphs into compact serialized programs, then executes them through a minimal runtime with hardware acceleration and on-device large language model inference capabilities. The project distinguishes itself through a hardware accelerator delegate system that partitions model subgraphs and offloads computation to specialized backends including NPUs, GPUs, and DSPs from Apple, Arm, Intel, MediaTek,

This project is a Chinese language translation of the technical guides and API references for the PyTorch deep learning framework. It serves as a localized knowledge base and reference material to make deep learning documentation accessible to non-English speakers. The documentation covers a comprehensive range of PyTorch capabilities, including neural network model development, automatic differentiation, and the implementation of backend kernels. It provides detailed guidance on distributed training strategies, model deployment through formats like ONNX and C++, and various model optimizatio

llm-compressor is a quantization toolkit and post-training library designed to reduce the memory footprint and size of large language models. It provides a framework for compressing models using weight and activation quantization to enable more efficient deployment. The project distinguishes itself through a distributed quantization framework that utilizes data-parallel processing and disk-based weight offloading to handle massive model checkpoints that exceed available system memory. It includes specialized compressors for diverse architectures, including Mixture-of-Experts, Vision-Language,

Neural Compressor is a deep learning model compression toolkit and AI inference acceleration engine. It functions as an automated model quantization tool and hardware-aware model compiler designed to reduce the memory footprint of neural networks and decrease execution latency. The project provides specialized frameworks for optimizing large language models, utilizing weight-only quantization and hardware-specific kernels to improve the operational efficiency of generative AI workloads. It maps neural network operators to specialized CPU and GPU vector instructions to accelerate model executi

LiteRT is a runtime and API for executing machine learning and generative AI models on mobile, desktop, and IoT hardware. It consists of an inference engine and a specialized environment for running quantized large language and diffusion models locally on edge hardware. The system includes an ahead-of-time model compiler that translates models into hardware-specific bytecode to reduce startup latency and memory overhead. It provides a unified interface for Neural Processing Units with automatic fallback routing to CPUs or GPUs when specific subgraph support is unavailable. An edge model conve

Segment Anything Fast is a high-performance computer vision inference engine and image segmentation framework built for PyTorch. It provides a specialized environment for automated object isolation and mask generation, designed to process large-scale visual datasets with increased throughput. The project distinguishes itself through a suite of system-level optimization strategies that accelerate deep learning model performance. By utilizing graph-based model compilation, just-in-time kernel fusion, and hardware-aware quantization, it reduces computational latency and memory footprint. These t