Awesome GitHub RepositoriesLogit Processors

Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference. The library features extensive support for model optimization and

Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It provides a programmable interface for orchestrating complex generation workflows, enabling developers to coordinate multi-turn dialogues, tool invocations, and reasoning chains through a domain-specific language. The platform is built to support production-scale deployments, offering an OpenAI-compatible API that allows for integration with existing application ecosystems. The system distinguishes itself through a disaggregated architecture that separates compute-intensive pr

This project is a cross-platform machine learning inference engine designed to execute pre-trained models across diverse operating systems and hardware environments. It functions as a standardized execution framework that manages the entire lifecycle of model inference, from loading and graph optimization to hardware-accelerated execution and generative sequence management. The runtime distinguishes itself through a highly modular architecture that decouples model logic from hardware-specific kernels. By utilizing an execution provider abstraction, it enables developers to offload computation

WebLLM is a library for executing large language models directly within web browsers. It provides a framework for building conversational artificial intelligence applications that perform inference locally, ensuring user data privacy by eliminating the need for external server dependencies. The project distinguishes itself by leveraging browser-native graphics APIs to perform intensive machine learning computations on the client side. It maintains application responsiveness by offloading heavy model tasks to background threads and ensures continuous operation through service workers that func

DeepGEMM is a suite of specialized GPU kernels and a just-in-time compiler designed for low-precision matrix operations, Mixture-of-Experts models, and attention processing. It provides a library of high-performance matrix multiplication kernels using FP8 precision to increase compute throughput and reduce memory usage. The project features a JIT CUDA kernel compiler that generates and loads optimized compute kernels at runtime to eliminate the need for manual compilation during installation. It includes specialized implementations for grouped matrix multiplication that process multiple group