Provides PyTorch AI/ML examples for Modal Context Protocol (MCP), Agent-to-Agent (A2A), RAG, and vLLM workflows, enabling reproducible and scalable pipelines for research and deployment.
Sponsored
Explore a comprehensive collection of Python and PyTorch AI/ML examples, meticulously crafted to showcase best practices in data preprocessing, model training, evaluation, and deployment. This repository focuses on Modal Context Protocol (MCP) for context-aware modeling, Agent-to-Agent (A2A) workflows, and accelerating large language models with vLLM, providing modular and reproducible pipelines suitable for both local development and cloud environments like Google Colab.
Características Principales
01Modal Context Protocol (MCP) Implementations
020 GitHub stars
03Reproducible and Modular Machine Learning Pipelines
04PyTorch AI/ML Examples for diverse workflows
05vLLM for Large Language Model Acceleration
06Agent-to-Agent (A2A) Workflow Integration
Casos de Uso
01Accelerating large language model inference and deployment
02Implementing agent-to-agent communication workflows