Awesome GitHub RepositoriesModel Serving

DeepPavlov is a conversational AI framework and deep learning NLP library designed for building end-to-end dialogue systems and chatbots. It functions as an NLP pipeline orchestrator that allows users to compose pre-trained models and text processing components into sequential data flows for complex linguistic tasks. The system is distinguished by its ability to act as a chatbot deployment server, exposing trained conversational models as web services via REST and Socket APIs. It utilizes JSON-based pipeline configurations and dynamic variable interpolation to decouple model logic from infras

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

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

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

Serge is a self-hosted web chat interface for running large language models locally using the llama.cpp inference engine. It loads GGUF-format model files directly on your own machine, removing the need for internet connectivity or external API keys, and streams responses to the browser in real time via WebSocket connections. The project is packaged for containerized deployment using Docker and Docker Compose, with a Traefik reverse proxy that handles HTTP and WebSocket routing along with automatic TLS certificate management. Ready-made Kubernetes manifests are also provided, enabling deploym

ToolBench is an open platform for training, serving, and evaluating large language models that retrieve and call real-world APIs to complete user instructions. It provides an API-aware inference engine that selects relevant tools from a large corpus and generates sequences of tool calls to produce final answers, along with a custom API registration system that lets users add their own REST endpoints for the model to discover and invoke. The platform includes a complete instruction-tuning pipeline for training models on curated tool-use data, a multi-tool execution engine that coordinates sequ

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

This is a webcam-based client for a local llama.cpp server that enables real-time object detection and vision-language model inference directly from a browser. It captures frames from the user's webcam at configurable intervals and sends them to a locally running inference server for analysis, displaying both detection results and textual scene descriptions as they are produced. The application distinguishes itself by combining object detection with vision-language scene description in a single real-time interface, all processed through a local llama.cpp server for private, offline operation.

本项目是一套 MLOps 架构指南和框架，旨在设计并将深度学习系统部署到生产环境。它为模型推理部署、机器学习流水线编排以及生产级机器学习架构的构建提供了结构化的方法。 该项目的特色在于专注于分布式深度学习和边缘 AI 优化。它涵盖了在多个 GPU 上并行化模型训练以处理大规模数据集的方法，并应用了量化和蒸馏等技术来减小嵌入式硬件上的模型体积。 其功能范围还扩展到了监控和可观测性，包括跟踪模型性能、数据漂移和实验指标。此外，它还解决了数据工作流编排、通过对象存储进行数据集版本控制，以及使用自适应批处理和容器化编排来管理高并发推理请求的问题。

SakuraLLM is a multi-format document translation system that hosts large language models for translating Japanese text into other languages. It functions as an inference server that exposes translation models through an OpenAI-compatible API, allowing any tool supporting the OpenAI client format to send translation requests. The system is designed as a glossary-aware translation engine that applies user-defined term dictionaries to ensure consistent translation of proper nouns and names across outputs. The project distinguishes itself by supporting multiple high-performance inference backends

gptme 是一个自主 AI 代理服务器和框架，专为本地系统自动化、软件开发和代码执行而设计。它作为本地执行引擎运行，使语言模型能够运行 Shell 命令、修改本地文件并与操作系统交互。 该项目充当 Model Context Protocol 客户端，与外部服务器集成，以使用标准化工具和数据源扩展代理功能。它具有提供程序无关的路由系统，可编排跨多个专有云 API 和本地 AI 后端的任务。 该系统包括用于无头浏览器自动化、视觉内容分析和用于映射代码库的符号代码分析的功能。为确保安全，它实现了人机协作（human-in-the-loop）护栏，在执行敏感系统更改或最终确定文件补丁之前需要用户确认。 该应用程序可以作为独立桌面二进制文件或通过 Docker 容器化进行部署。