关于
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.
主要功能
- 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
使用案例
- Accelerating large language model inference and deployment
- Implementing agent-to-agent communication workflows
- Developing context-aware AI models with MCP