OpenSource Llms: Uncensored & Secure Ai Locally With Rag

Open-Source Llms: Uncensored & Secure Ai Locally With Rag
Last updated 8/2024
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

Private ChatGPT Alternatives: Llama3, Mistral a. more with Function Calling, RAG, Vector Databases, LangChain, AI-Agents

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
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

Requirements
No prior knowledge is required; everything will be shown step by step.
It is advantageous to have a PC with a good graphics card, 16 GB RAM, and 6 GB VRAM (the Apple M series, Nvidia, and AMD are ideal), but this is not mandatory.

Description
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

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