Open-source LLMs- Uncensored secure AI locally with RAG

Open-source LLMs: Uncensored & secure AI locally with RAG, available at $54.99, has an average rating of 4.81, with 81 lectures, based on 158 reviews, and has 1696 subscribers.

You will learn about 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 This course is ideal for individuals who are To everyone who wants to learn something new and dive deep into open-source LLMs with RAG, Function Calling and AI-Agents or To entrepreneurs who want to become more efficient and save money or To developers, programmers, and tech enthusiasts or To anyone who doesnt want the restrictions of big tech companies and wants to use uncensored AI It is particularly useful for To everyone who wants to learn something new and dive deep into open-source LLMs with RAG, Function Calling and AI-Agents or To entrepreneurs who want to become more efficient and save money or To developers, programmers, and tech enthusiasts or To anyone who doesnt want the restrictions of big tech companies and wants to use uncensored AI.

Enroll now: Open-source LLMs: Uncensored & secure AI locally with RAG

Summary

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

Price: $54.99

Average Rating: 4.81

Number of Lectures: 81

Number of Published Lectures: 81

Number of Curriculum Items: 81

Number of Published Curriculum Objects: 81

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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 doesnt want the restrictions of big tech companies and wants to use uncensored AI

Target Audiences

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 doesnt want the restrictions of big tech companies and wants to use uncensored AI

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 LLMs

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.

Practical Application of Open-Source LLMs

The 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 Deployment

An 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 Databases

Learn 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 Agents

For 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 Tips

Finally, 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!

Course Curriculum

Chapter 1: Introduction and Overview

Lecture 1: Welcome

Lecture 2: Course Overview

Lecture 3: My Goal and Some Tips

Lecture 4: Explanation of the Links

Lecture 5: Important Links

Chapter 2: Why Open-Source LLMs? Differences, Advantages, and Disadvantages

Lecture 1: What is this Section about?

Lecture 2: What are LLMs like ChatGPT, Llama, Mistral, etc.

Lecture 3: Which LLMs are available and what should I use: Finding The Best LLMs

Lecture 4: Disadvantages of Closed-Source LLMs like ChatGPT, Gemini, and Claude

Lecture 5: Advantages and Disadvantages of Open-Source LLMs like Llama3, Mistral & more

Lecture 6: Recap: Dont Forget This!

Chapter 3: The Easiest Way to Run Open-Source LLMs Locally & What You Need

Lecture 1: Requirements for Using Open-Source LLMs Locally: GPU, CPU & Quantization

Lecture 2: Installing LM Studio and Alternative Methods for Running LLMs

Lecture 3: Using Open-Source Models in LM Studio: Llama 3, Mistral, Phi-3 & more

Lecture 4: 4 Censored vs. Uncensored LLMs: Llama3 with Dolphin Finetuning

Lecture 5: The Use Cases of classic LLMs like Phi-3 Llama and more

Lecture 6: Vision (Image Recognition) with Open-Source LLMs: Llama3, Llava & Phi3 Vision

Lecture 7: Some Examples of Image Recognition (Vision)

Lecture 8: More Details on Hardware: GPU Offload, CPU, RAM, and VRAM

Lecture 9: Summary of What You Learned & an Outlook to Lokal Servers & Prompt Engineering

Chapter 4: Prompt Engineering for Open-Source LLMs and Their Use in the Cloud

Lecture 1: HuggingChat: An Interface for Using Open-Source LLMs

Lecture 2: System Prompts: An Important Part of Prompt Engineering

Lecture 3: Why is Prompt Engineering Important? [A example]

Lecture 4: Semantic Association: The most Importnant Concept you need to understand

Lecture 5: The structured Prompt: Copy my Prompts

Lecture 6: Instruction Prompting and some Cool Tricks

Lecture 7: Role Prompting for LLMs

Lecture 8: Shot Prompting: Zero-Shot, One-Shot & Few-Shot Prompts

Lecture 9: Reverse Prompt Engineering and the OK Trick

Lecture 10: Chain of Thought Prompting: Let`s think Step by Step

Lecture 11: Tree of Thoughts (ToT) Prompting in LLMs

Lecture 12: The Combination of Prompting Concepts

Lecture 13: Creating Your Own Assistants in HuggingChat

Lecture 14: Groq: Using Open-Source LLMs with a Fast LPU Chip Instead of a GPU

Lecture 15: Recap: What You Should Remember

Chapter 5: Function Calling, RAG, and Vector Databases with Open-Source LLMs

Lecture 1: What Will Be Covered in This Section?

Lecture 2: What is Function Calling in LLMs

Lecture 3: Vector Databases, Embedding Models & Retrieval-Augmented Generation (RAG)

Lecture 4: Installing Anything LLM and Setting Up a Local Server for a RAG Pipeline

Lecture 5: Local RAG Chatbot with Anything LLM & LM Studio

Lecture 6: Function Calling with Llama 3 & Anything LLM (Searching the Internet)

Lecture 7: Function Calling, Summarizing Data, Storing & Creating Charts with Python

Lecture 8: Other Features of Anything LLM: TTS and External APIs

Lecture 9: Downloading Ollama & Llama 3, Creating & Linking a Local Server

Lecture 10: Recap Dont Forget This!

Chapter 6: Optimizing RAG Apps: Tips for Data Preparation

Lecture 1: What Will Be Covered in This Section: Better RAG, Data & Chunking

Lecture 2: Tips for Better RAG Apps: Firecrawl for Your Data from Websites

Lecture 3: More Efficient RAG with LlamaIndex & LlamaParse: Data Preparation for PDFs &more

Lecture 4: LlamaIndex Update: LlamaParse made easy!

Lecture 5: Chunk Size and Chunk Overlap for a Better RAG Application

Lecture 6: Recap: What You Learned in This Section

Chapter 7: Local AI Agents with Open-Source LLMs

Lecture 1: What Will Be Covered in This Section on AI Agents

Lecture 2: AI Agents: Definition & Available Tools for Creating Opensource AI-Agents

Lecture 3: We use Langchain with Flowise, Locally with Node.js

Lecture 4: Installing Flowise with Node.js (JavaScript Runtime Environment)

Lecture 5: The Flowise Interface for AI-Agents and RAG ChatBots

Lecture 6: Local RAG Chatbot with Flowise, LLama3 & Ollama: A Local Langchain App

Lecture 7: Our First AI Agent: Python Code & Documentation with Superwicer and 2 Worker

Lecture 8: AI Agents with Function Calling, Internet and Three Experts for Social Media

Lecture 9: Which AI Agent Should You Build & External Hosting with Render

Lecture 10: Chatbot with Open-Source Models from Huggingface & Embeddings in HTML (Mixtral)

Lecture 11: Insanely fast inference with the Groq API

Lecture 12: Recap What You Should Remember

Chapter 8: Finetuning, Renting GPUs, Open-Source TTS, Finding the BEST LLM & More Tips

Lecture 1: What Is This Section About?

Lecture 2: Text-to-Speech (TTS) with Google Colab

Lecture 3: Moshi Talk to an Open-Source AI

Lecture 4: Finetuning an Open-Source Model with Huggingface or Google Colab

Lecture 5: Finetuning Open-Source LLMs with Google Colab, Alpaca + Llama-3 8b from Unsloth

Lecture 6: What is the Best Open-Source LLM I Should Use?

Lecture 7: Llama 3.1 Infos and What Models should you use

Lecture 8: Grok from xAI

Lecture 9: Renting a GPU with Runpod or Massed Compute if Your Local PC Isnt Enough

Lecture 10: Recap: What You Should Remember!

Chapter 9: Data Privacy, Security, and What Comes Next?

Lecture 1: THE LAST SECTION: What is This About?

Lecture 2: Jailbreaks: Security Risks from Attacks on LLMs with Prompts

Lecture 3: Prompt Injections: Security Problem of LLMs

Lecture 4: Data Poisoning and Backdoor Attacks

Lecture 5: Data Privacy and Security: Is Your Data at Risk?

Lecture 6: Commercial Use and Selling of AI-Generated Content

Lecture 7: My Thanks and Whats Next?

Lecture 8: Bonus

Instructors

Arnold Oberleiter
Dein Dozent

Rating Distribution

1 stars: 1 votes

2 stars: 0 votes

3 stars: 7 votes

4 stars: 24 votes

5 stars: 126 votes

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