Fine Tuning LLM with Hugging Face Transformers for NLP

Do not take this course if you are an ML beginner. This course is designed for those who are interested in pure coding and want to fine-tune LL Ms instead of focusing on prompt engineering. Otherwise, you may find it difficult to understand.Welcome to "Mastering Transformer Models and LLM Fine Tuning", a comprehensive and practical course designed for all levels, from beginners to advanced practitioners in Natural Language Processing (NLP). This course delves deep into the world of Transformer models, fine-tuning techniques, and knowledge distillation, with a special focus on popular BERT variants like Phi2, LLAMA, T5, BERT, DistilBERT, MobileBERT, and TinyBERT.Course Overview:Section 1: Introduction Get an overview of the course and understand the learning outcomes.Introduction to the resources and code files you will need throughout the course.Section 2: Understanding Transformers with Hugging Face Learn the fundamentals of Hugging Face Transformers.Explore Hugging Face pipelines, checkpoints, models, and datasets.Gain insights into Hugging Face Spaces and Auto-Classes for seamless model management.Section 3: Core Concepts of Transformers and LL Ms Delve into the architectures and key concepts behind Transformers.Understand the applications of Transformers in various NLP tasks.Introduction to transfer learning with Transformers.Section 4: BERT Architecture Deep Dive Detailed exploration of BERT's architecture and its importance in context understanding.Learn about Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) in BERT.Understand BERT fine-tuning and evaluation techniques.Section 5: Practical Fine-Tuning with BERT</strong