Generative AI Fine Tuning LLM Models Crash Course
Krish Naik
@krishnaik06About
I am the Founder of KrishAI Technologies Private Limited and my experience is pioneering in machine learning, deep learning, and computer vision,an educator, and a mentor, with over 10 years' experience in the industry. This is my YouTube channel where I explain various topics on machine learning, deep learning, and AI with many real-world problem scenarios. I have delivered over 30 tech talks on data science, machine learning, and AI at various meet-ups, technical institutions, and community-arranged forums. My main aim is to make everyone familiar of ML and AI.Please subscribe and support the channel. As i love new technology, all these videos are free and I promise to make more interesting content as we go ahead. For any collaboration drop me a mail at [email protected] Please free to drop a mail for Product unboxing, GPU's unboxing and any other collaboration
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Video Description
This video is a crash course on understanding how finetuning on LLM models can be performed uing QLORA,LORA, Quantization using LLama2, Gradient and Google Gemma model. This crash course includes both theoretical intuition and practical intuition on making you understand how we can perform finetuning. Timestamp: 00:00:00 Introduction 00:01:20 Quantization Intuition 00:33:44 Lora And QLORA Indepth Intuition 00:56:07 Finetuning With LLama2 01:20:16 1 bit LLM Indepth Intuition 01:37:14 Finetuning with Google Gemma Models 01:59:26 Building LLm Pipelines With No code 02:20:14 Fine tuning With Own Cutom Data Code Github: https://github.com/krishnaik06/Finetuning-LLM ------------------------------------------------------------------------------------------------- Support me by joining membership so that I can upload these kind of videos https://www.youtube.com/channel/UCNU_lfiiWBdtULKOw6X0Dig/join ----------------------------------------------------------------------------------- ►Generative AI On AWS:https://www.youtube.com/watch?v=2WOa4_3Bgtw&list=PLZoTAELRMXVP5zpBfH7pab4aB1LbmCM1z ►Fresh Langchain Playlist: https://www.youtube.com/watch?v=KmQOlg5YfU0&list=PLZoTAELRMXVOQPRG7VAuHL--y97opD5GQ ►LLM Fine Tuning Playlist: https://www.youtube.com/watch?v=Vg3dS-NLUT4&list=PLZoTAELRMXVN9VbAx5I2VvloTtYmlApe3 ►AWS Bedrock Playlist: https://www.youtube.com/watch?v=2maPaQutcWs&list=PLZoTAELRMXVP8-wzKPtrRST3jNCprvMZj ►Llamindex Playlist: https://www.youtube.com/watch?v=1eym7BTnuNg&list=PLZoTAELRMXVNOWh1SDXt5NFujQMOt-CWy ►Google Gemini Playlist: https://www.youtube.com/watch?v=it0l6lx3qI0&list=PLZoTAELRMXVNbDmGZlcgCA3a8mRQp5axb&pp=gAQBiAQB ►Langchain Playlist: https://www.youtube.com/watch?v=4O1rs7mrNDo&list=PLZoTAELRMXVORE4VF7WQ_fAl0L1Gljtar&pp=gAQBiAQB ►Data Science Projects: https://www.youtube.com/watch?v=S_F_c9e2bz4&list=PLZoTAELRMXVPS-dOaVbAux22vzqdgoGhG&pp=iAQB ►Learn In One Tutorials Statistics in 6 hours: https://www.youtube.com/watch?v=LZzq1zSL1bs&t=9522s&pp=ygUVa3Jpc2ggbmFpayBzdGF0aXN0aWNz End To End RAG LLM APP Using LlamaIndex And OpenAI- Indexing And Querying Multiple Pdf's Machine Learning In 6 Hours: https://www.youtube.com/watch?v=JxgmHe2NyeY&t=4733s&pp=ygUba3Jpc2ggbmFpayBtYWNoaW5lIGxlYXJuaW5n Deep Learning 5 hours : https://www.youtube.com/watch?v=d2kxUVwWWwU&t=1210s&pp=ygUYa3Jpc2ggbmFpayBkZWVwIGxlYXJuaW5n ►Learn In a Week Playlist Statistics:https://www.youtube.com/watch?v=11unm2hmvOQ&list=PLZoTAELRMXVMgtxAboeAx-D9qbnY94Yay Machine Learning : https://www.youtube.com/watch?v=z8sxaUw_f-M&list=PLZoTAELRMXVPjaAzURB77Kz0YXxj65tYz Deep Learning:https://www.youtube.com/watch?v=8arGWdq_KL0&list=PLZoTAELRMXVPiyueAqA_eQnsycC_DSBns NLP : https://www.youtube.com/watch?v=w3coRFpyddQ&list=PLZoTAELRMXVNNrHSKv36Lr3_156yCo6Nn --------------------------------------------------------------------------------------------------- My Recording Gear Laptop: https://amzn.to/4886inY Office Desk : https://amzn.to/48nAWcO Camera: https://amzn.to/3vcEIHS Writing Pad:https://amzn.to/3OuXq41 Monitor: https://amzn.to/3vcEIHS Audio Accessories: https://amzn.to/48nbgxD Audio Mic: https://amzn.to/48nbgxD
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