#4

Building Chatbot Interfaces with Open Source Large Language Models

Deploy and Interact with LLMs through a WebUI: Participants will be able to independently set up and manage a WebUI for large language models, enabling them to perform tasks like text generation, answering queries, or any custom functionality defined during the course.
Build and Customize Chatbot Interfaces Using LLMs: By the end of the course, participants will have the skills to create interactive and responsive chatbots that utilize the power of LLMs to automate responses and engage with users effectively.
Understand Practical and Ethical Considerations of Deploying LLMs: Participants will learn about the implications of using LLMs, including data privacy, ethical usage, and the importance of unbiased training data to prevent perpetuating stereotypes or biases.

Practical Relevance: The course is designed to make cutting-edge AI technology accessible to a broader audience, empowering them with the ability to implement solutions that can enhance user engagement and automate responses using AI.
Hands-on Experience: Given the practical focus, participants will not only learn about LLMs in theory but also apply this knowledge by building real systems, which is crucial for cementing understanding and fostering innovation.
Emerging Importance: As AI continues to evolve and integrate into various sectors, understanding and being able to deploy LLMs is becoming an essential skill in many fields, including tech, customer service, and education.

This course aims to bridge the gap between theoretical knowledge of AI and practical application, ensuring participants are well-prepared to use these technologies responsibly and innovatively.

1. Understand the Architecture and Capabilities of Various Open-Source LLMs:

Objective: To provide a deep understanding of how large language models like OLlama 3, MixTRL, and WizardLM-2 7B are structured and function. This includes exploring their neural network architectures, training processes, and the unique features that distinguish each model.
Application: Through lectures and demonstrations, students will learn about the underlying technologies that power these models, including transformers and their variants, and how these models are adapted to handle different types of data and tasks.

2. Learn How to Deploy These Models Using a WebUI:

Objective: To equip participants with the practical skills needed to deploy LLMs using web-based interfaces. This involves training on setting up servers, integrating backend models with frontend services, and ensuring the system is user-friendly.
Application: Participants will engage in hands-on sessions where they will follow step-by- step instructions to configure and launch a WebUI that interacts with an LLM. This includes understanding the APIs that connect the model to the WebUI.

3. Develop a Basic Chatbot Interface Similar to ChatGPT for Practical Applications:

Objective: To teach participants how to build and customize their own chatbot interfaces that can engage users in conversation, similar to ChatGPT.
Application: The course will guide participants through the process of designing a chatbot, programming it to respond to queries, and customizing it for specific applications such as customer service, educational aids, or personal assistants.

Course Duration: 180 minutes

Course Structure

Time	Activity	Description
0:00 - 0:10	Introduction	Course overview, goals, and introduction to LLMs and their importance.
0:10 - 0:20	Overview of Open-Source LLMs	Discussing the features of OLlama 3, MixTRL, and WizardLM-2 7B.
0:20 - 0:30	Setting Up the Development Environment	Installing necessary tools and libraries for LLM deployment.
0:30 - 0:40	Introduction to WebUI	Basics of WebUI development and its role in interacting with LLMs.
0:40 - 0:50	Deploying OLlama 3	Step-by-step guide to setting up OLlama 3 with a basic WebUI.
0:50 - 1:00	Coffee Break	Short break for refreshments and networking.
1:00 - 1:10	Deploying MixTRL	Configuring MixTRL in a web interface and exploring its unique features.
1:10 - 1:20	Deploying WizardLM-2 7B	Setting up WizardLM-2 7B for interaction through WebUI.
1:20 - 1:30	Building a Basic Chat Interface	Introduction to creating a chatbot interface similar to ChatGPT.
1:30 - 1:40	Customizing the Chat Interface	Adding custom responses and functionality to the chatbot.
1:40 - 1:50	Lunch Break	Time for lunch and informal discussions.
1:50 - 2:00	Integrating OLlama 3 with the Chat Interface	Hands-on practice integrating OLlama 3 into the custom chat interface.
2:00 - 2:10	Integrating MixTRL with the Chat Interface	Applying MixTRL to the chatbot and testing its capabilities.
2:10 - 2:20	Integrating WizardLM-2 7B with the Chat Interface	Fine-tuning the chat interface to leverage the strengths of WizardLM-2 7B.
2:20 - 2:30	Testing and Debugging	Testing the chat interfaces with real-world scenarios and debugging.
2:30 - 2:40	Discussion on Ethical AI Usage	Exploring ethical considerations in using LLMs for public-facing applications.
2:40 - 2:50	Future Trends in LLM Development	Discussing upcoming trends and potential advancements in LLM technology.
2:50 - 3:00	Course Wrap-Up and Q&A	Summarizing key learnings, answering questions, and providing resources for further exploration.

Changes to speakers or content of conferences or courses will not be notified separately.