Open-source LLMs: Uncensored & secure AI locally with RAG
Open-source LLMs: Uncensored & secure AI locally with RAG
Private ChatGPT Alternatives: Llama3, Mistral a. more with Function Calling, RAG, Vector Databases, LangChain, AI-Agents
Product Brand: Udemy
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Udemy Coupon Code for Open-source LLMs: Uncensored & secure AI locally with RAG Course. Private ChatGPT Alternatives: Llama3, Mistral a. more with Function Calling, RAG, Vector Databases, LangChain, AI-Agents
Created by Arnold Oberleiter | 10 hours on-demand video course | 11 downloadable resources
Open-source LLMs Course Overview
Open-source LLMs: Uncensored & secure AI locally with RAG
This course provides a comprehensive introduction to the world of open-source LLMs. You’ll learn about the differences between open-source and closed-source models and discover why open-source LLMs are an attractive alternative. Topics such as ChatGPT, Llama, and Mistral will be covered in detail. Additionally, you’ll learn about the available LLMs and how to choose the best models for your needs. The course places special emphasis on the disadvantages of closed-source LLMs and the pros and cons of open-source LLMs like Llama3 and Mistral.
What you’ll learn
- Why Open-Source LLMs? Differences, Advantages, and Disadvantages of Open-Source and Closed-Source LLMs
- What are LLMs like ChatGPT, Llama, Mistral, Phi3, Qwen2-72B-Instruct, Grok, Gemma, etc.
- Which LLMs are available and what should I use? Finding “The Best LLMs”
- Requirements for Using Open-Source LLMs Locally
- Installation and Usage of LM Studio, Anything LLM, Ollama, and Alternative Methods for Operating LLMs
- Censored vs. Uncensored LLMs
- Finetuning an Open-Source Model with Huggingface or Google Colab
- Vision (Image Recognition) with Open-Source LLMs: Llama3, Llava & Phi3 Vision
- Hardware Details: GPU Offload, CPU, RAM, and VRAM
- All About HuggingChat: An Interface for Using Open-Source LLMs
- System Prompts in Prompt Engineering + Function Calling
- Prompt Engineering Basics: Semantic Association, Structured & Role Prompts
- Groq: Using Open-Source LLMs with a Fast LPU Chip Instead of a GPU
- Vector Databases, Embedding Models & Retrieval-Augmented Generation (RAG)
- Creating a Local RAG Chatbot with Anything LLM & LM Studio
- Linking Ollama & Llama 3, and Using Function Calling with Llama 3 & Anything LLM
- Function Calling for Summarizing Data, Storing, and Creating Charts with Python
- Using Other Features of Anything LLM and External APIs
- Tips for Better RAG Apps with Firecrawl for Website Data, More Efficient RAG with LlamaIndex & LlamaParse for PDFs and CSVs
- Definition and Available Tools for AI Agents, Installation and Usage of Flowise Locally with Node (Easier Than Langchain and LangGraph)
- Creating an AI Agent that Generates Python Code and Documentation, and Using AI Agents with Function Calling, Internet Access, and Three Experts
- Hosting and Usage: Which AI Agent Should You Build and External Hosting, Text-to-Speech (TTS) with Google Colab
- Finetuning Open-Source LLMs with Google Colab (Alpaca + Llama-3 8b, Unsloth)
- Renting GPUs with Runpod or Massed Compute
- Security Aspects: Jailbreaks and Security Risks from Attacks on LLMs with Jailbreaks, Prompt Injections, and Data Poisoning
- Data Privacy and Security of Your Data, as well as Policies for Commercial Use and Selling Generated Content
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Who this course is for
- To everyone who wants to learn something new and dive deep into open-source LLMs with RAG, Function Calling and AI-Agents
- To entrepreneurs who want to become more efficient and save money
- To developers, programmers, and tech enthusiasts
- To anyone who doesn’t want the restrictions of big tech companies and wants to use uncensored AI
