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Course Content
1. Introduction and Overview
0/5
1.Welcome .html
2.Course Overview
06:58
3.My Goal and Some Tips
04:37
4.Explanation of the Links
03:14
5.Important Links
2. Why Open-Source LLMs Differences, Advantages, and Disadvantages
0/6
1.What is this Section about?
00:34
2.What are LLMs like ChatGPT, Llama, Mistral, etc.
14:59
3.Which LLMs are available and what should I use: Finding “The Best LLMs”
07:16
4.Disadvantages of Closed-Source LLMs like ChatGPT, Gemini, and Claude
10:44
5.Advantages and Disadvantages of Open-Source LLMs like Llama3, Mistral
03:31
6.Recap: Don’t Forget This!
03:08
3. The Easiest Way to Run Open-Source LLMs Locally & What You Need
0/10
1. Requirements for Using Open-Source LLMs Locally: GPU, CPU & Quantization
07:28
2. Installing LM Studio and Alternative Methods for Running LLMs
08:10
3. Using Open-Source Models in LM Studio: Llama 3, Mistral, Phi-3 & more
18:51
4. 4 Censored vs. Uncensored LLMs: Llama3 with Dolphin Finetuning
07:54
5. The Use Cases of classic LLMs like Phi-3 Llama and more
05:50
6. Vision (Image Recognition) with Open-Source LLMs: Llama3, Llava & Phi3 Vision
08:07
7. Some Examples of Image Recognition (Vision)
08:40
8. More Details on Hardware: GPU Offload, CPU, RAM, and VRAM
05:42
8.1 Hardware .docx
9. Summary of What You Learned & an Outlook to Lokal Servers & Prompt Engineering
05:34
4. Prompt Engineering for Open-Source LLMs and Their Use in the Cloud
0/23
1.HuggingChat: An Interface for Using Open-Source LLMs
08:18
2. System Prompts: An Important Part of Prompt Engineering
07:53
3. Why is Prompt Engineering Important? [A example]
03:14
4. Semantic Association: The most Importnant Concept you need to understand
04:03
5. The structured Prompt: Copy my Prompts
06:01
5.1 1 The structured Prompt .odt
5.2 1 The structured Prompt .pdf
6. Instruction Prompting and some Cool Tricks
06:34
7. Role Prompting for LLMs
06:14
8. Shot Prompting: Zero-Shot, One-Shot & Few-Shot Prompts
07:27
9. Reverse Prompt Engineering and the “OK” Trick
09:43
9.1 2 Revers Prompt Engineering .odt
9.2 2 Revers Prompt Engineering .pdf
10. Chain of Thought Prompting: Let’s think Step by Step
08:14
11. Tree of Thoughts (ToT) Prompting in LLMs
10:42
12. The Combination of Prompting Concepts
06:44
12.1 3 Prompt Engineering Framework .odt
12.2 3 Prompt Engineering Framework .pdf
12.3 4 Prompt Generator .odt
12.4 4 Prompt Generator .pdf
13. Creating Your Own Assistants in HuggingChat
07:51
14. Groq: Using Open-Source LLMs with a Fast LPU Chip Instead of a GPU
04:11
15. Recap: What You Should Remember
02:49
5. Function Calling, RAG, and Vector Databases with Open-Source LLMs
0/10
1. What Will Be Covered in This Section?
01:44
2. What is Function Calling in LLMs
07:04
3. Vector Databases, Embedding Models & Retrieval-Augmented Generation (RAG)
11:58
4. Installing Anything LLM and Setting Up a Local Server for a RAG Pipeline
12:06
5. Local RAG Chatbot with Anything LLM & LM Studio
15:28
6. Function Calling with Llama 3 & Anything LLM (Searching the Internet)
07:53
7. Function Calling, Summarizing Data, Storing & Creating Charts with Python
07:58
8. Other Features of Anything LLM: TTS and External APIs
12:10
9. Downloading Ollama & Llama 3, Creating & Linking a Local Server
13:34
10. Recap Don’t Forget This!
03:53
6. Optimizing RAG Apps Tips for Data Preparation
0/5
1. What Will Be Covered in This Section: Better RAG, Data & Chunking
01:15
2. Tips for Better RAG Apps: Firecrawl for Your Data from Websites
04:27
3. More Efficient RAG with Llamalndex & LlamaParse: Data Preparation for PDFs &more
12:32
4. Chunk Size and Chunk Overlap for a Better RAG Application
08:57
5. Recap: What You Learned in This Section
02:35
7. Local AI Agents with Open-Source LLMs
0/12
1. What Will Be Covered in This Section on Al Agents
01:56
2. Al Agents: Definition & Available Tools for Creating Opensource Al-Agents
14:10
3. We use Langchain with Flowise, Locally with Node.js
05:42
4. Installing Flowise with Node.js (JavaScript Runtime Environment)
07:07
5. The Flowise Interface for Al-Agents and RAG ChatBots
05:25
6. Local RAG Chatbot with Flowise, LLama3 & Ollama: A Local Langchain App
16:39
7. Our First Al Agent: Python Code & Documentation with Superwicer and 2 Worker
15:08
8. Al Agents with Function Calling, Internet and Three Experts for Social Media
16:33
9. Which Al Agent Should You Build & External Hosting with Render
12:31
10. Chatbot with Open-Source Models from Huggingface & Embeddings in HTML (Mixtral)
08:37
11. Insanely fast inference with the Groq API
08:27
12. Recap What You Should Remember
04:29
8. Finetuning, Renting GPUs, Open-Source TTS, Finding the BEST LLM & More Tips
0/10
1. What Is This Section About?
02:06
2. Text-to-Speech (TTS) with Google Colab
08:38
3. Moshi Talk to an Open-Source Al
04:20
4. Finetuning an Open-Source Model with Huggingface or Google Colab
05:59
5. Finetuning Open-Source LLMs with Google Colab, Alpaca + Llama-3 8b from Unsloth
22:51
6. What is the Best Open-Source LLM I Should Use?
04:03
7. Llama 3.1 Infos and What Models should you use
03:42
8. Grok from xAl
04:09
9. Renting a GPU with Runpod or Massed Compute if Your Local PC Isn’t Enough
07:29
10. Recap: What You Should Remember!
06:06
9. Data Privacy, Security, and What Comes Next
0/8
1. THE LAST SECTION: What is This About?
01:50
2. Jailbreaks: Security Risks from Attacks on LLMs with Prompts
12:02
3. Prompt Injections: Security Problem of LLMs
08:31
4. Data Poisoning and Backdoor Attacks
03:45
5. Data Privacy and Security: Is Your Data at Risk?
08:13
6. Commercial Use and Selling of Al-Generated Content
08:40
7. My Thanks and What’s Next?
04:56
8. Bonus
Open-source LLMs: Uncensored & secure AI locally with RAG
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