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.
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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.
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.
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What's inside
Learning objectives
- 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
- Show more
- Show less
- 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
- Show more
- Show less
Syllabus
Introduction and Overview
Welcome
Course Overview
My Goal and Some Tips
Read more
Syllabus
Good to know
Save this course
Save
Activities
Compile a list of LLM Resources
Show steps
Keep your learning resources organized. Create a curated list of useful LLM-related resources (e.g., tutorials, documentation, articles)
Show steps
-
Search for LLM-related resources online using search engines, social media, or online forums.
-
Evaluate the relevance and quality of each resource.
-
Organize your resources into categories (e.g., tutorials, documentation, articles).
-
Share your list with others who may find it useful.
Review Basic Python Concepts
Show steps
Strengthen your foundation for working with LLMs by refreshing your knowledge of basic Python concepts.
Browse courses on
Python
Show steps
-
Review Python tutorials and documentation.
-
Complete practice exercises and coding challenges.
-
Build a small Python project to apply your refreshed knowledge.
Follow Tutorials on Open-Source LLMs
Show steps
Deepen your understanding of open-source LLMs by following tutorials that provide hands-on experience with their APIs and capabilities.
Browse courses on
Open Source LLMs
Show steps
-
Search for tutorials on open-source LLMs using search engines, online forums, or social media.
-
Choose a tutorial that aligns with your learning goals and skill level.
-
Follow the tutorial step-by-step and experiment with the LLM API.
-
Build a small project or experiment to apply what you learned from the tutorial.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend an LLM Workshop
Show steps
Learn from experts and connect with peers at an LLM workshop designed to further your understanding of the field.
Show steps
-
Research and identify LLM workshops that align with your learning goals.
-
Register for the workshop and prepare any necessary materials.
-
Attend the workshop and actively participate in the sessions.
-
Take notes, ask questions, and engage with the workshop facilitators and other attendees.
-
Follow up after the workshop by applying what you learned to your own projects and initiatives.
Practice LLM Prompt Engineering
Show steps
Mastering LLMs require fluency in giving useful instructions. Sharpen your abilities by practicing LLM prompt engineering.
Show steps
-
Identify the type of task you want to complete with the LLM (e.g., question answering, code generation).
-
Brainstorm a list of potential prompts that could be used to complete the task.
-
Choose the best prompt from your list and test it out with the LLM.
-
Evaluate the output of the LLM and make any necessary adjustments to your prompt.
-
Repeat steps 2-4 until you are satisfied with the output of the LLM.
Develop a Presentation on LLM Applications
Show steps
Expand your understanding of LLM applications by researching, analyzing, and presenting on their potential use cases in various industries and domains.
Show steps
-
Research and identify different industries and domains where LLMs can be applied.
-
Analyze the potential benefits and challenges of using LLMs in each industry or domain.
-
Develop a presentation that clearly communicates your findings.
-
Deliver your presentation to an audience of peers, instructors, or industry professionals.
Write a Blog Post on LLM Ethics
Show steps
Develop a nuanced understanding of LLM ethics by researching, analyzing, and writing a comprehensive blog post on the topic.
Browse courses on
AI Ethics
Show steps
-
Research different ethical considerations related to the use of LLMs.
-
Analyze the potential benefits and risks of LLMs in society.
-
Write a blog post that clearly presents your findings and perspectives on LLM ethics.
-
Publish your blog post on a relevant platform and share it with others.
Build an LLM-powered Chatbot
Show steps
Solidify your understanding of LLMs by designing, developing, and deploying your very own LLM-powered chatbot.
Show steps
-
Choose an LLM provider and API.
-
Design the chatbot's user interface and functionality.
-
Develop the chatbot's backend code.
-
Deploy the chatbot and test its functionality.
-
Monitor the chatbot's performance and make improvements as needed.
Compile a list of LLM Resources
Show steps
Keep your learning resources organized. Create a curated list of useful LLM-related resources (e.g., tutorials, documentation, articles)
Show steps
- Search for LLM-related resources online using search engines, social media, or online forums.
- Evaluate the relevance and quality of each resource.
- Organize your resources into categories (e.g., tutorials, documentation, articles).
- Share your list with others who may find it useful.
Review Basic Python Concepts
Show steps
Strengthen your foundation for working with LLMs by refreshing your knowledge of basic Python concepts.
Browse courses on
Python
Show steps
- Review Python tutorials and documentation.
- Complete practice exercises and coding challenges.
- Build a small Python project to apply your refreshed knowledge.
Follow Tutorials on Open-Source LLMs
Show steps
Deepen your understanding of open-source LLMs by following tutorials that provide hands-on experience with their APIs and capabilities.
Browse courses on
Open Source LLMs
Show steps
- Search for tutorials on open-source LLMs using search engines, online forums, or social media.
- Choose a tutorial that aligns with your learning goals and skill level.
- Follow the tutorial step-by-step and experiment with the LLM API.
- Build a small project or experiment to apply what you learned from the tutorial.
Attend an LLM Workshop
Show steps
Learn from experts and connect with peers at an LLM workshop designed to further your understanding of the field.
Show steps
- Research and identify LLM workshops that align with your learning goals.
- Register for the workshop and prepare any necessary materials.
- Attend the workshop and actively participate in the sessions.
- Take notes, ask questions, and engage with the workshop facilitators and other attendees.
- Follow up after the workshop by applying what you learned to your own projects and initiatives.
Practice LLM Prompt Engineering
Show steps
Mastering LLMs require fluency in giving useful instructions. Sharpen your abilities by practicing LLM prompt engineering.
Show steps
- Identify the type of task you want to complete with the LLM (e.g., question answering, code generation).
- Brainstorm a list of potential prompts that could be used to complete the task.
- Choose the best prompt from your list and test it out with the LLM.
- Evaluate the output of the LLM and make any necessary adjustments to your prompt.
- Repeat steps 2-4 until you are satisfied with the output of the LLM.
Develop a Presentation on LLM Applications
Show steps
Expand your understanding of LLM applications by researching, analyzing, and presenting on their potential use cases in various industries and domains.
Show steps
- Research and identify different industries and domains where LLMs can be applied.
- Analyze the potential benefits and challenges of using LLMs in each industry or domain.
- Develop a presentation that clearly communicates your findings.
- Deliver your presentation to an audience of peers, instructors, or industry professionals.
Write a Blog Post on LLM Ethics
Show steps
Develop a nuanced understanding of LLM ethics by researching, analyzing, and writing a comprehensive blog post on the topic.
Browse courses on
AI Ethics
Show steps
- Research different ethical considerations related to the use of LLMs.
- Analyze the potential benefits and risks of LLMs in society.
- Write a blog post that clearly presents your findings and perspectives on LLM ethics.
- Publish your blog post on a relevant platform and share it with others.
Build an LLM-powered Chatbot
Show steps
Solidify your understanding of LLMs by designing, developing, and deploying your very own LLM-powered chatbot.
Show steps
- Choose an LLM provider and API.
- Design the chatbot's user interface and functionality.
- Develop the chatbot's backend code.
- Deploy the chatbot and test its functionality.
- Monitor the chatbot's performance and make improvements as needed.
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