LLM Recipes is a collection of end‑to‑end projects, tutorials, and reference implementations for working with modern large language models and multimodal systems.
It is designed as a practical playground: start from a clean Ubuntu install, bring up local or cloud models, and build real applications across text, speech, vision, agents, and robots.

The repo is organised as a set of recipes:

• Application projects (e.g. self‑hosted audiobooks, shopping assistants, autonomous warehouse dispatcher)
• Tutorials that walk from “hello world” REST APIs to function calling, RAG, vision, speech, and deployment
• Infrastructure and deployment guides for GPUs, Docker, vLLM, llama.cpp, GH200, etc.

If you want to learn by building, this repo is meant to be your lab notebook.

• RAG & retrieval

  • Dense embeddings and vector search (e.g. Pinecone, pgvector)
  • Hybrid retrieval (BM25 + dense), re‑rankers, and DSPy‑style RAG graphs
  • Case studies like the AI Tutor RAG system (see case-studies/ai-tutor)

• Agents

  • Code‑focused agents such as Qwen3 Coder (agents/qwen3-coder)
  • Multi‑agent and decision agents (Factorio, Aquila, Mars‑style patterns)

• Speech & audio

  • Whisper‑based speech‑to‑text and speech‑to‑speech assistants (python/assistant)
  • NotebookLM‑style audiobooks: parsing scripts and generating TTS for books/long‑form content (python/notebooklm)
  • Indic/low‑resource TTS (Sarvam, ai4bharat) and Kimi‑style audio experiments

• Vision & multimodal

  • Image/scene understanding and VLMs (e.g. LLaVA, Moondream, Pixtral, Meta Llama vision)
  • VideoBook‑style pipelines such as Divya Drishti (python/divya-drishti)
  • Drone + VLM reconnaissance for battlefield mapping (python/reconaissance)

• Quantisation, inference, and deployment

  • tutorials/llama-cpp: quantisation and GGUF workflows with llama.cpp
  • tutorials/vllm, tutorials/gh200: high‑performance inference and GPU setup
  • Docker‑based deployment, Nvidia Container Toolkit, Ollama and Open WebUI integration

• Reinforcement learning & robotics

  • RL environments and frameworks (reinforcement_learning/)
  • Bhoomi and related robots (robots/) for embodied AI and trajectory planning

• Regional and Indic AI

  • Kannada language experiments (kannada/)
  • Indic translation and TTS (tutorials/sarvam, tutorials/indic)

• Bhoomi & robots

  • Robotics experiments, trajectory planners, and platform notes
  • See robots/README.md for the Bhoomi and biryani Bot documentation

• Notebook LLaMA (self‑hosted NotebookLM)

  • Path: python/notebooklm
  • Self‑hosted platform for turning manuscripts and documents into improved audiobooks using TTS

• Assistant & speech‑to‑speech

  • Path: python/assistant
  • REST API layer over local/remote LLMs, text+vision querying, Whisper‑based speech APIs, and speech‑to‑speech inference

• Quantisation tutorials

  • Path: tutorials/llama-cpp
  • Recipes for quantising models to GGUF and running them with llama.cpp

• Shopping Bot

  • Path: python/shopping-bot
  • Food ordering assistant combining Pinecone + LlamaIndex with BM25 and vector retrieval

• Divya Drishti (VideoBook)

  • Path: python/divya-drishti
  • Generates visual stories (e.g. Ramayana) with Stable Diffusion and Indic TTS, orchestrated by LLM prompts

• Reconnaissance (Drishti)

  • Path: python/reconaissance
  • Drone + VLM system for mapping and describing environments using open‑weight models

For a broader index of experiments and small projects, browse python/, tutorials/, and case-studies/.

• python/: main Python projects (assistant, notebooklm, shopping‑bot, divya‑drishti, reconaissance, aquila, etc.)
• tutorials/: topic‑oriented tutorials (REST APIs, function calling, RAG, vision, whisper, vLLM, GH200, llama‑cpp, Android, Indic AI, etc.)
• docs/: written docs (setup guides, deployment, quantisation, hackathons, dspy, vllm, interview prep)
• agents/: agent deployments (e.g. Qwen3 Coder, Factorio)
• case-studies/: design docs and RAG case studies (AI Tutor, insurance agent)
• reinforcement_learning/: RL experiments and notes
• robots/: Bhoomi and related robots, hardware notes
• kannada/: Kannada‑focused language experiments
• ui/: autonomous warehouse UI and voice dispatcher

• Follow docs/clean-ubuntu-setup.md to:
  • Install Ubuntu tooling and VS Code
  • Install Docker and Nvidia Container Toolkit
  • Install CUDA (where applicable) and GPU drivers

git clone https://github.com/<your-org-or-user>/llm-recipes.git
cd llm-recipes

• New to LLMs?
  Start with the tutorial progression in docs/tutorials.md (from simple REST APIs and local models up to speech, vision, and GUIs).

• Want a full application?

  • python/notebooklm – self‑hosted audiobooks platform
  • python/assistant – multi‑modal assistant with text, vision, and speech
  • python/shopping-bot – retrieval‑augmented shopping assistant

• Interested in deployment/perf?

  • tutorials/llama-cpp – quantisation and running models with llama.cpp
  • tutorials/vllm and tutorials/gh200 – vLLM and GPU deployment recipes

Each project directory typically includes its own README or notebook explaining local setup and usage.

• Tutorial index: see docs/tutorials.md for the progression from v0 (REST API + local LLM) through v9 (quantisation, YOLO, etc.).
• Many tutorials live under tutorials/ and are grouped by topic or provider (e.g. tutorials/mistral, tutorials/vision, tutorials/whisper, tutorials/dspy).

• Ubuntu + Docker + Nvidia setup: docs/clean-ubuntu-setup.md
• Deployment, quantisation, DSPy, vLLM, etc.: various docs under docs/
• Hackathons and challenges: docs/hackathons.md

• Thanks to the contributors and maintainers of the third‑party libraries, models, and tools used in this project.

This project is licensed under the MIT License – see the LICENSE file for details.