Open-source LLMs Uncensored & secure AI locally with RAG

Free Download Open-source LLMs Uncensored & secure AI locally with RAG
Published 7/2024
Created by Arnold Oberleiter
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 75 Lectures ( 9h 23m ) | Size: 7.1 GB

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
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
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:
ChatGPT is useful, but have you noticed that there are many censored topics, you are pushed in certain political directions, some harmless questions go unanswered, and our data might not be secure with OpenAI? This is where open-source LLMs like Llama3, Mistral, Grok, Falkon, Phi3, and Command R+ can help!Are you ready to master the nuances of open-source LLMs and harness their full potential for various applications, from data analysis to creating chatbots and AI agents? Then this course is for you!Introduction to Open-Source LLMsThis 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.Practical Application of Open-Source LLMsThe course guides you through the simplest way to run open-source LLMs locally and what you need for this setup. You will learn about the prerequisites, the installation of LM Studio, and alternative methods for operating LLMs. Furthermore, you will learn how to use open-source models in LM Studio, understand the difference between censored and uncensored LLMs, and explore various use cases. The course also covers finetuning an open-source model with Huggingface or Google Colab and using vision models for image recognition.Prompt Engineering and Cloud DeploymentAn important part of the course is prompt engineering for open-source LLMs. You will learn how to use HuggingChat as an interface, utilize system prompts in prompt engineering, and apply both basic and advanced prompt engineering techniques. The course also provides insights into creating your own assistants in HuggingChat and using open-source LLMs with fast LPU chips instead of GPUs.Function Calling, RAG, and Vector DatabasesLearn what function calling is in LLMs and how to implement vector databases, embedding models, and retrieval-augmented generation (RAG). The course shows you how to install Anything LLM, set up a local server, and create a RAG chatbot with Anything LLM and LM Studio. You will also learn to perform function calling with Llama 3 and Anything LLM, summarize data, store it, and visualize it with Python.Optimization and AI AgentsFor optimizing your RAG apps, you will receive tips on data preparation and efficient use of tools like LlamaIndex and LlamaParse. Additionally, you will be introduced to the world of AI agents. You will learn what AI agents are, what tools are available, and how to install and use Flowise locally with Node.js. The course also offers practical insights into creating an AI agent that generates Python code and documentation, as well as using function calling and internet access.Additional Applications and TipsFinally, the course introduces text-to-speech (TTS) with Google Colab and finetuning open-source LLMs with Google Colab. You will learn how to rent GPUs from providers like Runpod or Massed Compute if your local PC isn’t sufficient. Additionally, you will explore innovative tools like Microsoft Autogen and CrewAI and how to use LangChain for developing AI agents.Harness the transformative power of open-source LLM technology to develop innovative solutions and expand your understanding of their diverse applications. Sign up today and start your journey to becoming an expert in the world of large language models!
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
Homepage

https://www.udemy.com/course/open-source-llms-uncensored-secure-ai-locally-with-rag/