Awesome GitHub RepositoriesModel Serving Interfaces

This project is a platform for the deployment of open source large language and multimodal models. It provides a unified interface to serve text, image, and speech models across local or cloud hardware. The system enables distributed AI inference by orchestrating model workloads across multiple nodes and devices. It includes a unified API adapter layer to standardize inputs and outputs, as well as tools for multimodal chat and structural image generation. The platform covers a broad capability surface including request batching for throughput optimization, dynamic model loading, and integrat

lmdeploy is a high-performance inference engine and deployment framework for large language models and vision models. It functions as a multi-modal model server and compression toolkit designed to serve models with high throughput and low latency. The system enables the distribution of model services across multiple machines using request-based load balancing and tensor parallelism. It includes specialized tools for model quantization and compression to reduce the memory footprint of weights and caches. The framework covers broad capability areas including production deployment, distributed

This project is a self-hosted system for discovering, browsing, and receiving personalized recommendations from academic papers on arXiv. It combines an arXiv API client that downloads paper metadata and PDFs with a TF-IDF document similarity engine and an SVM-based recommendation system that trains a classifier per user based on their preferences. The system provides a web interface for browsing, searching, and filtering recent arXiv submissions, alongside personalized paper recommendations generated from individual user signals. It also includes a Twitter mention tracker that periodically p

LightLLM is a high-performance serving framework for deploying and executing large language models. It functions as a multi-GPU inference engine and server capable of handling dense architectures, mixture-of-experts designs, and multimodal models that process both text and images. The system is distinguished by its specialized support for Mixture-of-Experts models using expert parallelism and fused kernels. It implements structured text generation through deterministic state machines and pushdown automata to enforce precise output formats. To optimize throughput, the framework employs specula

VILA is a vision-language model integration that combines a visual encoder with a large language model to process images and text in a shared space. Its primary purpose is to enable the generation of natural language explanations and detailed text summaries of images and videos based on user prompts. The project utilizes a multi-stage alignment pipeline to synchronize visual and textual embeddings through sequential pretraining and supervised fine-tuning. To support deployment on desktop and edge hardware, it employs quantized low-precision inference to reduce model weights to 4-bit precision

FastDeploy is a high-performance deployment framework for large language models, vision models, and multimodal models. It provides the infrastructure to launch model services that process combined image, video, and text inputs, exposing these capabilities through a standardized, OpenAI-compatible API for chat and text completions. The project distinguishes itself through advanced inference pipeline engineering and GPU optimization. It employs speculative decoding, tensor parallelism, and a disaggregated execution model that separates prefill and decode phases across different hardware resourc

vllm-omni is a high-throughput serving engine and distributed inference framework designed for omni-modal models. It serves as a multi-modal model API server capable of generating text, image, video, and audio data, providing a standardized interface for remote client access. The system features a non-autoregressive generation engine for parallel media production and a robot policy inference server that acts as a real-time communication bridge to robotic hardware using specialized protocols. It supports hybrid execution models that combine sequential token generation with parallelized media g