🍳 AI Recipe Genius — The Ultimate AI Cookbook

Created with ❤️ by Navuluri Balaji
A living collection of AI recipes, fine-tuning hacks, architectural deep dives, and agentic frameworks — distilled into one place for builders, researchers, and AI enthusiasts.

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✨ Introduction

AI Recipe Genius is not just a repository — it’s a cookbook for AI builders.
Think of it as your culinary lab for AI 🍲 where each recipe is a practical guide:

• 🧑‍🍳 Fine-tuning large & small models (SLMs, LLMs)
• 🧠 Understanding LLM architectures (Transformers, MoE, CoT, etc.)
• 🤖 Building custom Agentic frameworks and real-world use cases
• 🎛️ Exploring advanced training concepts (RL, RLHF, RHLHF, etc.)
• ☁️ Deploying models seamlessly (Cloud, Docker, Ollama, Edge devices)

Whether you’re just starting out or pushing the boundaries of AI research, this repo is your go-to kitchen for AI experiments, deployment recipes, and reusable patterns.

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🛠️ Technology Stack

• Models: LLMs, SLMs, MoE architectures, fine-tuned transformers
• Frameworks: PyTorch, TensorFlow, Hugging Face, LangChain, LangGraph, CrewAI, Autogen, ADK, nbagents
• Backend: FastAPI, Flask
• Frontend: Streamlit, React (for interactive demos)
• Libraries: transformers, torch, tensorflow, numpy, pandas, scikit-learn, requests
• Containerization: Docker, Kubernetes
• Deployment: Cloud (GCP, AWS, Azure), Ollama, Local GPUs/TPUs

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📚 Contents (AI Recipe Index)

This repo is structured like a cookbook — with each section containing recipes, tutorials, and code snippets:

🍴 Core AI Recipes

• Fine-tuning LLMs & SLMs
• Instruction tuning & adapters (LoRA, PEFT, QLoRA)
• MoE (Mixture of Experts) deep dives
• Chain of Thought (CoT), ReAct, and reasoning strategies
• RL, RLHF, RHLHF explained with hands-on code

🥘 Agentic Recipes

• Building custom AI agents from scratch
• Using frameworks (LangChain, CrewAI, Autogen, ADK, LangGraph, nbagents)
• Multi-agent collaboration patterns
• Real-world agentic use cases (customer support, research agents, workflow automation, etc.)

🍲 Deployment Recipes

• Containerizing AI apps with Docker
• Running on Cloud (GCP, AWS, Azure)
• Lightweight local deployments with Ollama
• Scaling inference & serving with FastAPI

🍛 Architecture Recipes

• Under-the-hood of Transformers & LLMs
• Memory, Attention, and Scaling Laws
• Efficient fine-tuning for low-resource environments
• Trade-offs between SLMs & LLMs

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🤝 Contributing

We welcome contributions!

• Fork the repo 🍴
• Create a feature branch 🌱
• Submit a PR 🚀

See CONTRIBUTING.md for detailed guidelines.

By contributing, you agree to follow our Code of Conduct.

🌟 Vision

The goal of this repo is to become a comprehensive AI knowledge hub —A place where builders learn, experiment, and share recipes that power the next generation of AI systems.

⚡ From Fine-tuning → Agentic Frameworks → Real-world Deployments ⚡