{"id":1692,"date":"2023-06-09T17:34:07","date_gmt":"2023-06-09T12:04:07","guid":{"rendered":"https:\/\/www.analyticsvidhya.com\/datahack-summit-2023\/?page_id=1692"},"modified":"2023-08-04T10:42:57","modified_gmt":"2023-08-04T05:12:57","slug":"mastering-llms-training-fine-tuning-and-best-practices","status":"publish","type":"page","link":"https:\/\/www.analyticsvidhya.com\/dhs-2023\/workshop\/mastering-llms-training-fine-tuning-and-best-practices\/","title":{"rendered":"Mastering LLMs: Training, Fine-tuning, and Best Practices"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Are you a frequent user of ChatGPT and Bard? Ever wondered about the magic that powers these remarkable technologies? It&#8217;s all thanks to the incredible models known as Large Language Models (LLMs). LLMs have revolutionized the field of Natural Language Processing (NLP) and are the driving force behind countless NLP applications. The world of LLMs is buzzing with groundbreaking research and exciting advancements within the community. Brace yourself for an immersive workshop where we delve deep into the realm of LLMs, exploring their various types and uncovering the cutting-edge architectures that define the current state of the art. Get ready to unlock the secrets of LLMs and witness the future of language technology unfold before your eyes!<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Here are the detailed module wise details-<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">Module 0: Introduction to LLMs<\/span><\/h4>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">History of LLMs<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What are LLMs?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Why LLMs?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What are the different types of LLMs?<\/span>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Continuing the text<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Dialogue Optimized<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h4><span style=\"font-weight: 400;\">Module 1: Understand the current state of the art of LLMs<\/span><\/h4>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Transformers<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">BERT<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">GPT and its variants<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">ChatGPT<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Bard<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">LIMA<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Falcon<\/span><\/li>\n<\/ul>\n<h4><span style=\"font-weight: 400;\">Module 2: Training LLMs and their Best Practices<\/span><\/h4>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Build vs Buy Pretrained LLM models?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Understand the scaling laws<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What are the challenges while training LLMs from scratch?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">How to train LLMs from scratch?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Pretrain LLM on a domain specific datasets<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">How to evaluate LLMs?<\/span><\/li>\n<\/ul>\n<h4><span style=\"font-weight: 400;\">Module 3: Finetuning LLMs<\/span><\/h4>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">How can we use LLMs on the downstream tasks?<\/span>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Prompting<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Fine Tuning<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Learn about prompt engineering and its techniques.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Learn about different finetuning techniques<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Finetune LLM on a downstream tasks<\/span><\/li>\n<\/ul>\n<h4><span style=\"font-weight: 400;\">Module 4: Parameter Efficient Fine Tuning\u00a0<\/span><\/h4>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Why and What is Parameter Efficient Fine Tuning (PEFT)?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Understanding different PEFT techniques<\/span>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Prefix Tuning<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">LoRA<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">QLoRA<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Finetune LLM on a single GPU using PEFT techniques<\/span><\/li>\n<\/ul>\n<h4><span style=\"font-weight: 400;\">Module 5: AutoGPT, LangChain, Vector DBs<\/span><\/h4>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Work hands-on with popular LLM tools <\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Work hands-on frameworks like AutoGPT, LangChain and Vector DBs<\/span><\/li>\n<\/ul>\n<p><strong>Pre-requisites:<\/strong><\/p>\n<ul>\n<li>System Requirement and Setup\n<ul>\n<li>Laptop with at least 4-8 GB of RAM<\/li>\n<li>We will be using a cloud jupyter notebook powered by GPU for the workshop<\/li>\n<\/ul>\n<\/li>\n<li>Offline Setup [Optional]\n<ul>\n<li>GPU good to have!<\/li>\n<li>Install Python3.9 or higher version(<a href=\"https:\/\/www.python.org\/downloads\/\" target=\"_blank\" rel=\"noopener\">Resource<\/a>)<\/li>\n<li>Install jupyter notebook (<a href=\"https:\/\/jupyter.org\/install\" target=\"_blank\" rel=\"noopener\">Resource<\/a>)<\/li>\n<\/ul>\n<\/li>\n<li>Pre-reads\n<ul>\n<li>Programming knowledge in Python (<a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2016\/01\/complete-tutorial-learn-data-science-python-scratch-2\/\" target=\"_blank\" rel=\"noopener\">Resource<\/a>)<\/li>\n<li>Jupyter Notebook Environment familiarity (<a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2018\/05\/starters-guide-jupyter-notebook\/\" target=\"_blank\" rel=\"noopener\">Resource<\/a>)<\/li>\n<li>Basics of Machine Learning and Deep Learning (<a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2017\/09\/common-machine-learning-algorithms\/\" target=\"_blank\" rel=\"noopener\">Resource<\/a>,<a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2017\/09\/common-machine-learning-algorithms\/\" target=\"_blank\" rel=\"noopener\">Resource<\/a>)<\/li>\n<li>Fundamentals of NLP(<a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2017\/01\/ultimate-guide-to-understand-implement-natural-language-processing-codes-in-python\/\" target=\"_blank\" rel=\"noopener\">Resource1<\/a>,<a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2017\/06\/word-embeddings-count-word2veec\/\" target=\"_blank\" rel=\"noopener\">Resource2<\/a>)<\/li>\n<li>Familiarity with Pytorch(<a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2020\/07\/how-to-train-an-image-classification-model-in-pytorch-and-tensorflow\/\" target=\"_blank\" rel=\"noopener\">Resource<\/a>)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Note: These are tentative details and are subject to change.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Are you a frequent user of ChatGPT and Bard? Ever wondered about the magic that powers these remarkable technologies? It&#8217;s all thanks to the incredible models known as Large Language Models (LLMs). LLMs have revolutionized the field of Natural Language Processing (NLP) and are the driving force behind countless NLP applications. The world of LLMs [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2573,"parent":890,"menu_order":8,"comment_status":"closed","ping_status":"closed","template":"workshop-detail.php","meta":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Mastering LLMs: Training, Fine-tuning, and Best Practices - DataHack Summit 2023<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.analyticsvidhya.com\/dhs-2023\/workshop\/mastering-llms-training-fine-tuning-and-best-practices\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Mastering LLMs: Training, Fine-tuning, and Best Practices - DataHack Summit 2023\" \/>\n<meta property=\"og:description\" content=\"Are you a frequent user of ChatGPT and Bard? 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