Fine Tuning LLM with Hugging Face Transformers for NLP

<div>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 LLMs instead of focusing on prompt engineering. Otherwise, you may find it difficult to understand.</div><div><br></div><div>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.</div><div><br></div><div>Course Overview:</div><div><br></div><div>Section 1: Introduction</div><div><ul><li><span style="font-size: 1rem;">Get an overview of the course and understand the learning outcomes.</span></li><li><span style="font-size: 1rem;">Introduction to the resources and code files you will need throughout the course.</span></li></ul></div><div><span style="font-size: 1rem;">Section 2: Understanding Transformers with Hugging Face</span></div><div><ul><li><span style="font-size: 1rem;">Learn the fundamentals of Hugging Face Transformers.</span></li><li><span style="font-size: 1rem;">Explore Hugging Face pipelines, checkpoints, models, and datasets.</span></li><li><span style="font-size: 1rem;">Gain insights into Hugging Face Spaces and Auto-Classes for seamless model management.</span></li></ul></div><div><span style="font-size: 1rem;">Section 3: Core Concepts of Transformers and LLMs</span></div><div><ul><li><span style="font-size: 1rem;">Delve into the architectures and key concepts behind Transformers.</span></li><li><span style="font-size: 1rem;">Understand the applications of Transformers in various NLP tasks.</span></li><li><span style="font-size: 1rem;">Introduction to transfer learning with Transformers.</span></li></ul></div><div><span style="font-size: 1rem;">Section 4: BERT Architecture Deep Dive</span></div><div><ul><li><span style="font-size: 1rem;">Detailed exploration of BERT's architecture and its importance in context understanding.</span></li><li><span style="font-size: 1rem;">Learn about Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) in BERT.</span></li><li><span style="font-size: 1rem;">Understand BERT fine-tuning and evaluation techniques.</span></li></ul></div><div><span style="font-size: 1rem;">Section 5: Practical Fine-Tuning with BERT</span></div><div><ul><li><span style="font-size: 1rem;">Hands-on sessions to fine-tune BERT for sentiment classification on Twitter data.</span></li><li><span style="font-size: 1rem;">Step-by-step guide on data loading, tokenization, and model training.</span></li><li><span style="font-size: 1rem;">Practical application of fine-tuning techniques to build a BERT classifier.</span></li></ul></div><div><span style="font-size: 1rem;">Section 6: Knowledge Distillation Techniques for BERT</span></div><div><ul><li><span style="font-size: 1rem;">Introduction to knowledge distillation and its significance in model optimization.</span></li><li><span style="font-size: 1rem;">Detailed study of DistilBERT, including loss functions and paper walkthroughs.</span></li><li><span style="font-size: 1rem;">Explore MobileBERT and TinyBERT, with a focus on their unique distillation techniques and practical implementations.</span></li></ul></div><div><span style="font-size: 1rem;">Section 7: Applying Distilled BERT Models for Real-World Tasks like Fake News Detection</span></div><div><ul><li><span style="font-size: 1rem;">Use DistilBERT, MobileBERT, and TinyBERT for fake news detection.</span></li><li><span style="font-size: 1rem;">Practical examples and hands-on exercises to build and evaluate models.</span></li><li><span style="font-size: 1rem;">Benchmarking performance of distilled models against BERT-Base.</span></li></ul></div><div><span style="font-size: 1rem;">Section 8: Named Entity Recognition (NER) with DistilBERT</span></div><div><ul><li><span style="font-size: 1rem;">Techniques for fine-tuning DistilBERT for NER in restaurant search applications.</span></li><li><span style="font-size: 1rem;">Detailed guide on data preparation, tokenization, and model training.</span></li><li><span style="font-size: 1rem;">Hands-on sessions to build, evaluate, and deploy NER models.</span></li></ul></div><div><span style="font-size: 1rem;">Section 9: Custom Summarization with T5 Transformer</span></div><div><ul><li><span style="font-size: 1rem;">Practical guide to fine-tuning the T5 model for summarization tasks.</span></li><li><span style="font-size: 1rem;">Detailed walkthrough of dataset analysis, tokenization, and model fine-tuning.</span></li><li><span style="font-size: 1rem;">Implement summarization predictions on custom data.</span></li></ul></div><div><span style="font-size: 1rem;">Section 10: Vision Transformer for Image Classification</span></div><div><ul><li><span style="font-size: 1rem;">Introduction to Vision Transformers (ViT) and their applications.</span></li><li><span style="font-size: 1rem;">Step-by-step guide to using ViT for classifying Indian foods.</span></li><li><span style="font-size: 1rem;">Practical exercises on image preprocessing, model training, and evaluation.</span></li></ul></div><div><span style="font-size: 1rem;">Section 11: Fine-Tuning Large Language Models on Custom Datasets</span></div><div><ul><li><span style="font-size: 1rem;">Theoretical insights and practical steps for fine-tuning large language models (LLMs).</span></li><li><span style="font-size: 1rem;">Explore various fine-tuning techniques, including PEFT, LORA, and QLORA.</span></li><li><span style="font-size: 1rem;">Hands-on coding sessions to implement custom dataset fine-tuning for LLMs.</span></li></ul></div><div><span style="font-size: 1rem;">Section 12: Specialized Topics in Transformer Fine-Tuning</span></div><div><ul><li><span style="font-size: 1rem;">Learn about advanced topics such as 8-bit quantization and adapter-based fine-tuning.</span></li><li><span style="font-size: 1rem;">Review and implement state-of-the-art techniques for optimizing Transformer models.</span></li><li><span style="font-size: 1rem;">Practical sessions to generate product descriptions using fine-tuned models.</span></li></ul></div><div><span style="font-size: 1rem;">Section 13: Building Chat and Instruction Models with LLAMA</span></div><div><ul><li><span style="font-size: 1rem;">Learn about advanced topics such as 4-bit quantization and adapter-based fine-tuning.</span></li><li><span style="font-size: 1rem;">Techniques for fine-tuning the LLAMA base model for chat and instruction-based tasks.</span></li><li><span style="font-size: 1rem;">Practical examples and hands-on guidance to build, train, and deploy chat models.</span></li><li><span style="font-size: 1rem;">Explore the significance of chat format datasets and model configuration for PEFT fine-tuning.</span></li></ul></div><div><span style="font-size: 1rem;">Enroll now in "Mastering Transformer Models and LLM Fine Tuning on Custom Dataset" and gain the skills to harness the power of state-of-the-art NLP models. Whether you're just starting or looking to enhance your expertise, this course offers valuable knowledge and practical experience to elevate your proficiency in the field of natural language processing.</span></div><div><br></div><div>Unlock the full potential of Transformer models with our comprehensive course. Master fine-tuning techniques for BERT variants, explore knowledge distillation with DistilBERT, MobileBERT, and TinyBERT, and apply advanced models like RoBERTa, ALBERT, XLNet, and Vision Transformers for real-world NLP applications. Dive into practical examples using Hugging Face tools, T5 for summarization, and learn to build custom chat models with LLAMA.</div><div><br></div><div>Keywords: Transformer models, fine-tuning BERT, DistilBERT, MobileBERT, TinyBERT, RoBERTa, ALBERT, XLNet, ELECTRA, ConvBERT, DeBERTa, Vision Transformer, T5, BART, Pegasus, GPT-3, DeiT, Swin Transformer, Hugging Face, NLP applications, knowledge distillation, custom chat models, LLAMA.</div>

What you'll learn:

• Understand transformers and their role in NLP.
• Gain hands-on experience with Hugging Face Transformers.
• Learn about relevant datasets and evaluation metrics.
• Fine-tune transformers for text classification, question answering, natural language inference, text summarization, and machine translation.
• Understand the principles of transformer fine-tuning.
• Apply transformer fine-tuning to real-world NLP problems.
• Learn about different types of transformers, such as BERT, GPT-2, and T5.
• Hands-on experience with the Hugging Face Transformers library