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HelloPython AI ML

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.

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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

  • Modal Context Protocol (MCP) Implementations
  • 0 GitHub stars
  • Reproducible and Modular Machine Learning Pipelines
  • PyTorch AI/ML Examples for diverse workflows
  • vLLM for Large Language Model Acceleration
  • Agent-to-Agent (A2A) Workflow Integration

Casos de Uso

  • Accelerating large language model inference and deployment
  • Implementing agent-to-agent communication workflows
  • Developing context-aware AI models with MCP
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