Open-source LLMs: Uncensored & secure AI locally with RAG Course Syllabus

Overview: This comprehensive course provides a hands-on journey into building secure, uncensored AI systems using open-source LLMs. You'll learn to deploy models locally, implement RAG pipelines, create AI agents, fine-tune models, and enforce strict security and privacy standards. With over 6 hours of practical content, the course blends theory with real-world tools like Ollama, LM Studio, Flowise, and LlamaIndex, culminating in a final project that integrates everything you've learned.

Module 1: Why Open-Source LLMs

Estimated time: 0.5 hours

• Compare open-source vs closed-source LLMs: ownership, censorship, and cost implications
• Explore advantages and limitations of open-source models
• Survey popular open LLMs: Llama3, Mistral, Grok, Phi-3, Gemma, Qwen
• Evaluate use cases for uncensored AI in enterprise and personal projects

Module 2: Local Deployment & Tools

Estimated time: 1 hour

• Install and configure LM Studio, Ollama, and Anything LLM locally
• Run LLMs on CPU and GPU: hardware requirements and performance trade-offs
• Distinguish between censored and uncensored models
• Set up local inference environments for privacy-first AI

Module 3: Prompt Engineering & Function Calling

Estimated time: 1 hour

• Master system prompts, structured prompting, and few-shot learning
• Apply chain-of-thought and role-based prompting techniques
• Implement function calling in Llama3 for dynamic responses
• Build data pipelines using function-calling in Anything LLM

Module 4: RAG & Vector Databases

Estimated time: 1.25 hours

• Build a local RAG chatbot using LM Studio and a local embedding store
• Integrate vector databases for efficient similarity search
• Use Firecrawl for web scraping and data ingestion
• Process PDFs and CSVs with LlamaIndex and LlamaParse

Module 5: AI Agents & Flowise

Estimated time: 1 hour

• Define AI agents and their role in autonomous workflows
• Set up multi-agent systems using Flowise locally
• Create agents that generate Python code and documentation
• Connect agents to external APIs and tools

Module 6: Final Project

Estimated time: 2 hours

• Design and deploy a secure, self-hosted RAG-powered chatbot
• Incorporate uncensored LLMs with fine-tuned behavior via prompt engineering
• Implement security best practices: input validation, content filtering, and access control

Prerequisites

• Basic understanding of Python and command-line tools
• Familiarity with AI/ML concepts (no advanced math required)
• Access to a computer with at least 16GB RAM (GPU recommended but not required)

What You'll Be Able to Do After

• Deploy open-source LLMs locally with full data privacy
• Build custom RAG pipelines using vector databases and document parsers
• Create intelligent AI agents using Flowise and function calling
• Fine-tune models using Google Colab and manage GPU resources
• Secure AI systems against prompt injections, jailbreaks, and data leaks