What will you in Open-source LLMs: Uncensored & secure AI locally with RAG Course
Explore the advantages and limitations of open-source vs closed-source LLMs (e.g., Llama, Mistral, Phi‑3, Qwen)
Install and run LLMs locally using tools like LM Studio, Ollama, and Anything LLM
Build custom RAG pipelines with vector databases, embedding models, and function calling
Employ prompt-engineering strategies, system prompts, and agents (e.g., Flowise)
Fine‑tune models (Alpaca, Llama‑3) via Google Colab and manage hardware and GPU usage
Understand AI security: jailbreaks, prompt injections, data poisoning, and privacy risks
Program Overview
Module 1: Why Open-Source LLMs
⏳ 30 minutes
Compare open- and closed-source model pros/cons (ownership, censorship, cost)
Survey popular open LLMs: Llama3, Mistral, Grok, Phi‑3, Gemma, Qwen
Module 2: Local Deployment & Tools
⏳ 60 minutes
Set up LM Studio, Anything LLM, Ollama locally using CPU/GPU; hardware requirements explained
Distinguish between censored vs uncensored models
Module 3: Prompt Engineering & Function Calling
⏳ 60 minutes
Learn system prompts, structured prompts, few-shot, chain-of-thought techniques
Use function-calling in Llama3 and Anything LLM for chatbots and data pipelines
Module 4: RAG & Vector Databases
⏳ 75 minutes
Build local RAG chatbot using LM Studio and embedding store
Integrate Firecrawl (web scraping), LlamaIndex/LlamaParse for PDF/CSV ingestion
Module 5: AI Agents & Flowise
⏳ 60 minutes
Define AI agents and set up multi-agent workflows with Flowise locally
Create intelligent agents that generate Python code, documentation, and interface with APIs
Module 6: Fine‑Tuning & GPU Rental
⏳ 60 minutes
Fine-tune on Alpaca and Llama‑3 via Google Colab; information on using Runpod or Massed Compute
Module 7: TTS, Hosting & Extras
⏳ 45 minutes
Implement text-to-speech (TTS) solutions using Colab; self-hosting options and agent selection advice
Module 8: Security, Privacy & Scaling
⏳ 45 minutes
Learn about jailbreaks, prompt injections, data poisoning, and content leakage risks
Explore commercial policies, data privacy, and secure deployment best practices
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Job Outlook
High demand for engineers skilled in self-hosted, privacy-focused AI, particularly for RAG and LLM agents
Fostered careers in AI infrastructure, data engineering, and developer tooling
Salary potential: $110K–$180K+ for LLM engineering roles with RAG and security focus
Freelance paths include custom RAG solutions, privacy-first chatbot deployment, and AI-agent consulting
Specification: Open-source LLMs: Uncensored & secure AI locally with RAG
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