BEGIN:VCALENDAR VERSION:2.0 PRODID:-//HasGeek//NONSGML Funnel//EN DESCRIPTION:From Conceptual Understanding to Real-World Application: Maste r RAGs & LLM Fine-Tuning in 2 Days X-WR-CALDESC:From Conceptual Understanding to Real-World Application: Mast er RAGs & LLM Fine-Tuning in 2 Days NAME:2 day- LLM Applications:- RAG & Fine-Tuning Deep Dive: Theory to Depl oyment Workshop X-WR-CALNAME:2 day- LLM Applications:- RAG & Fine-Tuning Deep Dive: Theory to Deployment Workshop REFRESH-INTERVAL;VALUE=DURATION:PT12H SUMMARY:2 day- LLM Applications:- RAG & Fine-Tuning Deep Dive: Theory to D eployment Workshop TIMEZONE-ID:Asia/Kolkata X-PUBLISHED-TTL:PT12H X-WR-TIMEZONE:Asia/Kolkata BEGIN:VEVENT SUMMARY:2 day- LLM Applications:- RAG & Fine-Tuning Deep Dive: Theory to D eployment Workshop DTSTART:20240323T043000Z DTEND:20240324T103000Z DTSTAMP:20260421T183651Z UID:session/KAyQsXEuQNwsJyQumgx5Co@hasgeek.com SEQUENCE:14 CREATED:20240214T173639Z DESCRIPTION:# LLM FINE-TUNING\nDive into the world of Large Language Model s (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 se gments: theoretical knowledge and practical hands-on experience\, ensuring a comprehensive learning journey.\n\n### Theoretical Learning:\nIn this s egment\, participants will gain in-depth insights into the foundational an d advanced concepts essential for fine-tuning LLMs effectively. The steps covered include:\n\nData Generation Techniques: Explore various strategies for generating and curating datasets tailored for fine-tuning LLMs\, emph asizing the importance of data quality and relevance.\n\nLLM Selection: Le arn how to choose the right LLM for your specific project needs\, consider ing factors such as model size\, complexity\, and the task at hand.\n\nFin e-Tuning Techniques: Dive into advanced fine-tuning methods such as Low-Ra nk Adaptation (LoRA)\, Parameter-Efficient Fine-Tuning (PEFT)\, and others \, understanding their applications and benefits.\n\nHyperparameter Tuning & Training Strategies: Unpack the nuances of selecting optimal hyperparam eters and employing effective training strategies to maximize model perfor mance.\n\nEvaluation: Master the techniques for evaluating your fine-tuned model's performance\, using a range of metrics to assess accuracy\, effic iency\, and applicability to real-world tasks.\n\n### Practical Hands-On E xperience:\nFollowing the theoretical foundation\, participants will apply their newly acquired knowledge through a series of practical exercises\, covering:\n\nData Generation using Code: Implement code to generate or pre process datasets\, preparing them for the fine-tuning process.\n\nCode Imp lementation: Get hands-on experience with the actual implementation of fin e-tuning techniques on selected LLMs\, applying LoRA\, PEFT\, and more.\n\ nCreating a UI: Design and develop a user interface for interacting with y our fine-tuned model\, making it accessible for real-world testing and dem onstration.\n\nDeploying on Server: Learn the steps to deploy your model a nd its UI on a server\, ensuring it's ready for live interaction and scala ble to user demand.\n\nChatting with Your Own Server: Test the effectivene ss of your fine-tuned model by interacting with it through the UI\, evalua ting its responses\, and gaining insights into further optimization needs. \n\n# RAGs: Theory & Implementation\nHaving explored the intricacies of fi ne-tuning Large Language Models (LLMs) in our previous sessions\, we're ex cited to take you further into the AI frontier with our comprehensive work shop on **"Retrieval-Augmented Generation (RAG) "**. Building on the foun dational knowledge you've acquired\, this next step will unlock new potent ials in AI applications\, blending the theoretical depth with practical\, hands-on implementation strategies\n \n## Theory: Mastering the Foundation s \n\nWhat is RAG? Discover the innovative framework that combines the bes t of retrieval-based and generative AI models to produce more accurate\, c ontextually relevant responses. RAGs leverage vast databases of informatio n\, retrieving relevant documents to inform and enhance the generation pro cess.\nBuilding Blocks of RAGs: Unpack the components that make RAGs so po werful. Learn about the seamless integration of neural retrieval mechanism s with state-of-the-art generative models to improve answer quality and re levance.\n\nVector Databases: Explore the backbone of the retrieval proces s in RAG systems. Understand what vector databases are\, how they store an d manage high-dimensional data\, and why they are crucial for efficiently retrieving information in RAG implementations.\nEmbedding Models: Delve in to the world of embedding models\, the engines that transform text into nu merical representations. Discover how these models capture the essence of language in a form that machines can understand\, enabling the precise ret rieval of information based on semantic similarity rather than keyword mat ching.\n\nSelecting Embedding Models and Vector Databases: Learn the crite ria for choosing the right embedding model and vector database for your sp ecific needs. We'll cover the factors that influence these decisions\, inc luding accuracy\, scalability\, and domain specificity\, to ensure you can tailor your RAG implementation for optimal performance.\n\n## Implementat ion: Bringing RAGs to Life\nEnd-to-End Implementation with Proprietary LLM s: Step by step\, we'll guide you through integrating RAGs with your propr ietary large language models. From setting up the infrastructure to fine-t uning the models for your specific use cases\, you'll gain hands-on experi ence in building sophisticated AI systems.\n\nRAGs with Open Source LLMs: Not everyone has access to proprietary models\, but that doesn't limit you r 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.\n\nCreati ng a Chat UI: Learn how to build an intuitive chat interface that allows u sers to interact with your RAG-powered system. This session will cover the essentials of UI design and development\, ensuring a seamless user experi ence.\n\nDeployment on the Server: Get your RAG system up and running for the world to see. We'll cover deployment strategies\, server setup\, and s calability considerations\, ensuring your system is robust\, responsive\, and ready for real-world use.\n\nChat with Your Documents: The ultimate te st of a RAG system is its ability to understand and respond to queries wit h information retrieved from your documents. Experience the thrill of inte racting with your AI\, querying your own corpus of information\, and recei ving precise\, informative answers.\n\nThis workshop is your pathway to no t only understanding the theoretical aspects of LLM fine-tuning but also a pplying 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 s kills needed to fine-tune and deploy LLMs\, setting the stage for innovati on and advancement in your AI projects. Join us to unlock the full potenti al of LLMs and elevate your expertise to new heights.\n\nFor further queri es\, please write to us at support@hasgeek.com or call us at +91 7676 33 2 020. LAST-MODIFIED:20240308T072132Z LOCATION:Online - https://hasgeek.com/mahathib/llm-fine-tuning-deep-dive-t heory-to-deployment-workshop/ ORGANIZER;CN="mahathi bhagavatula":MAILTO:no-reply@hasgeek.com URL:https://hasgeek.com/mahathib/llm-fine-tuning-deep-dive-theory-to-deplo yment-workshop/ BEGIN:VALARM ACTION:display DESCRIPTION:2 day- LLM Applications:- RAG & Fine-Tuning Deep Dive: Theory to Deployment Workshop in 5 minutes TRIGGER:-PT5M END:VALARM END:VEVENT END:VCALENDAR