2024 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 LLMs 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 LLMs

• 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

• Hands-on sessions to fine-tune BERT for sentiment classification on Twitter data.

• Step-by-step guide on data loading, tokenization, and model training.

• Practical application of fine-tuning techniques to build a BERT classifier.

Section 6: Knowledge Distillation Techniques for BERT

• Introduction to knowledge distillation and its significance in model optimization.

• Detailed study of DistilBERT, including loss functions and paper walkthroughs.

• Explore MobileBERT and TinyBERT, with a focus on their unique distillation techniques and practical implementations.

Section 7: Applying Distilled BERT Models for Real-World Tasks like Fake News Detection

• Use DistilBERT, MobileBERT, and TinyBERT for fake news detection.

• Practical examples and hands-on exercises to build and evaluate models.

• Benchmarking performance of distilled models against BERT-Base.

Section 8: Named Entity Recognition (NER) with DistilBERT

• Techniques for fine-tuning DistilBERT for NER in restaurant search applications.

• Detailed guide on data preparation, tokenization, and model training.

• Hands-on sessions to build, evaluate, and deploy NER models.

Section 9: Custom Summarization with T5 Transformer

• Practical guide to fine-tuning the T5 model for summarization tasks.

• Detailed walkthrough of dataset analysis, tokenization, and model fine-tuning.

• Implement summarization predictions on custom data.

Section 10: Vision Transformer for Image Classification

• Introduction to Vision Transformers (ViT) and their applications.

• Step-by-step guide to using ViT for classifying Indian foods.

• Practical exercises on image preprocessing, model training, and evaluation.

Section 11: Fine-Tuning Large Language Models on Custom Datasets

• Theoretical insights and practical steps for fine-tuning large language models (LLMs).

• Explore various fine-tuning techniques, including PEFT, LORA, and QLORA.

• Hands-on coding sessions to implement custom dataset fine-tuning for LLMs.

Section 12: Specialized Topics in Transformer Fine-Tuning

• Learn about advanced topics such as 8-bit quantization and adapter-based fine-tuning.

• Review and implement state-of-the-art techniques for optimizing Transformer models.

• Practical sessions to generate product descriptions using fine-tuned models.

Section 13: Building Chat and Instruction Models with LLAMA

• Learn about advanced topics such as 4-bit quantization and adapter-based fine-tuning.

• Techniques for fine-tuning the LLAMA base model for chat and instruction-based tasks.

• Practical examples and hands-on guidance to build, train, and deploy chat models.

• Explore the significance of chat format datasets and model configuration for PEFT fine-tuning.

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.

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.

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.

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• 4 stars: 5 votes
• 5 stars: 46 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!