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2 day- LLM Applications:- RAG & Fine-Tuning Deep Dive: Theory to Deployment Workshop

2 day- LLM Applications:- RAG & Fine-Tuning Deep Dive: Theory to Deployment Workshop

From Conceptual Understanding to Real-World Application: Master RAGs & LLM Fine-Tuning in 2 Days

LLM FINE-TUNING

Dive into the world of Large Language Models (LLMs) with our one-day intensive workshop, “LLM Fine-Tuning Mastery: A Hands-On Online Workshop.” This program is meticulously designed for data scientists and AI enthusiasts who are keen on advancing their skills and understanding of fine-tuning LLMs. Our workshop is divided into two segments: theoretical knowledge and practical hands-on experience, ensuring a comprehensive learning journey.

Theoretical Learning:

In this segment, participants will gain in-depth insights into the foundational and advanced concepts essential for fine-tuning LLMs effectively. The steps covered include:

Data Generation Techniques: Explore various strategies for generating and curating datasets tailored for fine-tuning LLMs, emphasizing the importance of data quality and relevance.

LLM Selection: Learn how to choose the right LLM for your specific project needs, considering factors such as model size, complexity, and the task at hand.

Fine-Tuning Techniques: Dive into advanced fine-tuning methods such as Low-Rank Adaptation (LoRA), Parameter-Efficient Fine-Tuning (PEFT), and others, understanding their applications and benefits.

Hyperparameter Tuning & Training Strategies: Unpack the nuances of selecting optimal hyperparameters and employing effective training strategies to maximize model performance.

Evaluation: Master the techniques for evaluating your fine-tuned model’s performance, using a range of metrics to assess accuracy, efficiency, and applicability to real-world tasks.

Practical Hands-On Experience:

Following the theoretical foundation, participants will apply their newly acquired knowledge through a series of practical exercises, covering:

Data Generation using Code: Implement code to generate or preprocess datasets, preparing them for the fine-tuning process.

Code Implementation: Get hands-on experience with the actual implementation of fine-tuning techniques on selected LLMs, applying LoRA, PEFT, and more.

Creating a UI: Design and develop a user interface for interacting with your fine-tuned model, making it accessible for real-world testing and demonstration.

Deploying on Server: Learn the steps to deploy your model and its UI on a server, ensuring it’s ready for live interaction and scalable to user demand.

Chatting with Your Own Server: Test the effectiveness of your fine-tuned model by interacting with it through the UI, evaluating its responses, and gaining insights into further optimization needs.

RAGs: Theory & Implementation

Having explored the intricacies of fine-tuning Large Language Models (LLMs) in our previous sessions, we’re excited to take you further into the AI frontier with our comprehensive workshop on "Retrieval-Augmented Generation (RAG) ". Building on the foundational knowledge you’ve acquired, this next step will unlock new potentials in AI applications, blending the theoretical depth with practical, hands-on implementation strategies

Theory: Mastering the Foundations

What is RAG? Discover the innovative framework that combines the best of retrieval-based and generative AI models to produce more accurate, contextually relevant responses. RAGs leverage vast databases of information, retrieving relevant documents to inform and enhance the generation process.
Building Blocks of RAGs: Unpack the components that make RAGs so powerful. Learn about the seamless integration of neural retrieval mechanisms with state-of-the-art generative models to improve answer quality and relevance.

Vector Databases: Explore the backbone of the retrieval process in RAG systems. Understand what vector databases are, how they store and manage high-dimensional data, and why they are crucial for efficiently retrieving information in RAG implementations.
Embedding Models: Delve into the world of embedding models, the engines that transform text into numerical representations. Discover how these models capture the essence of language in a form that machines can understand, enabling the precise retrieval of information based on semantic similarity rather than keyword matching.

Selecting Embedding Models and Vector Databases: Learn the criteria for choosing the right embedding model and vector database for your specific needs. We’ll cover the factors that influence these decisions, including accuracy, scalability, and domain specificity, to ensure you can tailor your RAG implementation for optimal performance.

Implementation: Bringing RAGs to Life

End-to-End Implementation with Proprietary LLMs: Step by step, we’ll guide you through integrating RAGs with your proprietary large language models. From setting up the infrastructure to fine-tuning the models for your specific use cases, you’ll gain hands-on experience in building sophisticated AI systems.

RAGs with Open Source LLMs: Not everyone has access to proprietary models, but that doesn’t limit your ability to leverage RAG technology. We’ll show you how to implement RAGs using open-source large language models, ensuring you can build powerful, cutting-edge systems without the need for expensive licenses.

Creating a Chat UI: Learn how to build an intuitive chat interface that allows users to interact with your RAG-powered system. This session will cover the essentials of UI design and development, ensuring a seamless user experience.

Deployment on the Server: Get your RAG system up and running for the world to see. We’ll cover deployment strategies, server setup, and scalability considerations, ensuring your system is robust, responsive, and ready for real-world use.

Chat with Your Documents: The ultimate test of a RAG system is its ability to understand and respond to queries with information retrieved from your documents. Experience the thrill of interacting with your AI, querying your own corpus of information, and receiving precise, informative answers.

This workshop is your pathway to not only understanding the theoretical aspects of LLM fine-tuning but also applying this knowledge in creating real-world AI solutions. By the end of the day, you will have a solid grasp of both the concepts and practical skills needed to fine-tune and deploy LLMs, setting the stage for innovation and advancement in your AI projects. Join us to unlock the full potential of LLMs and elevate your expertise to new heights.

For further queries, please write to us at support@hasgeek.com or call us at +91 7676 33 2020.


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