2025 Fine Tuning LLM with Hugging Face Transformers for NLP from Udemy

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 Phi

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
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 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

What's inside

Learning objectives

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

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

Syllabus

Introduction

Course Introduction

Code File [Resources]

Hello Transformers

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides hands-on experience fine-tuning BERT for sentiment classification on Twitter data, offering practical skills applicable to real-world NLP problems

Explores knowledge distillation techniques using DistilBERT, MobileBERT, and TinyBERT, which are valuable for optimizing model performance and reducing computational costs

Covers advanced topics like 8-bit quantization and adapter-based fine-tuning, which are state-of-the-art techniques for optimizing Transformer models

Requires a solid foundation in machine learning, so beginners may find it challenging to grasp the core concepts and practical applications

Focuses on coding and fine-tuning LLMs, which may not be suitable for learners primarily interested in prompt engineering or high-level applications

Uses Hugging Face Transformers, which is a popular library, but learners should be aware that the library is updated frequently

Reviews summary

Practical llm fine-tuning with hugging face

According to learners, this course offers a largely practical and hands-on approach to fine-tuning LLMs and Transformer models using Hugging Face. Many appreciate the practical coding labs and concrete examples provided for tasks like sentiment analysis, NER, and summarization. While the course description warns it's not for beginners, some students still find it challenging, suggesting a solid prerequisite understanding of ML and NLP is indeed necessary. The coverage of various models, including BERT variants, T5, and LLaMA, is seen as a strength, although some wish for deeper dives into theory or optimization techniques. Overall, it's viewed as a valuable resource for those ready to dive into the implementation details of fine-tuning.

Strong on practice, lighter on deep theory.

"More focused on the 'how-to' than the deep 'why' behind the models."

"Wish there was a bit more theoretical background provided alongside the practical steps."

"Great for implementation, but if you're looking for deep mathematical theory, this might not be enough."

"Balanced approach between explaining concepts and showing how to code them."

"Relies heavily on using the Hugging Face library without getting bogged down in low-level details."

Covers recent models and techniques.

"Covers recent techniques like 4-bit quantization and PEFT for LLMs."

"It feels current with the focus on models like LLaMA and adapter fine-tuning."

"Appreciated seeing topics like Vision Transformers included."

"The '2025' in the title seems appropriate, covering modern LLM finetuning."

Code is generally clear and helpful.

"The code examples provided were clear and easy to follow along with."

"Step-by-step guides for data loading, tokenization, and training were well done."

"Found the practical sessions with code implementation very helpful."

"Code files matched the lectures well and were crucial for the hands-on parts."

"The Streamlit app example for prediction was a nice touch."

Explores various Transformer models and techniques.

"Appreciate the coverage of BERT variants, DistilBERT, and even LLaMA fine-tuning."

"Liked learning about knowledge distillation techniques and applying different models."

"The sections on T5 and Vision Transformers were interesting additions."

"Comprehensive overview of different models available through Hugging Face."

"Covers a good range of topics from basic Transformers to LLM fine-tuning."

Emphasizes hands-on coding and real-world tasks.

"The hands-on coding and projects are the strongest part of the course for me."

"I really liked that this wasn't just theory; we got to fine-tune models on actual datasets for different tasks."

"Provides concrete examples for tasks like sentiment analysis and NER, which is very useful."

"Learned practical skills I can immediately apply to my work."

"Excellent practical guide to fine-tuning models with Hugging Face."

Definitely not for beginners; prerequisite is real.

"Do not take this course if you are an ML beginner. This course is designed for those who are interested in pure coding..."

"As the course description states, this is NOT for beginners. You need a solid ML/NLP background."

"Found the pace quite fast and assumed prior knowledge I didn't fully have."

"It was challenging at times, definitely requires some comfort with code and ML concepts beforehand."

"Wish the prerequisites were emphasized even more; some parts felt overwhelming without prior experience."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in 2025 Fine Tuning LLM with Hugging Face Transformers for NLP with these activities:

Review Transformer Architectures

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Solidify your understanding of transformer architectures before diving into fine-tuning. This will help you grasp the underlying principles and make the fine-tuning process more intuitive.

Browse courses on Transformer Architecture

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Review the original Transformer paper.
Summarize the key components of the architecture.
Explain the purpose of attention mechanisms.

Read 'Hugging Face Transformers, Neural Networks'

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Gain a deeper understanding of Transformers and the Hugging Face library. This book will provide practical examples and insights to enhance your learning.

View Putting Knowledge to Work on Amazon

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Read the chapters related to fine-tuning and knowledge distillation.
Experiment with the code examples provided in the book.
Compare the book's approach to the course materials.

Read 'Natural Language Processing with Transformers'

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Gain a deeper understanding of Transformers and the Hugging Face library. This book will provide practical examples and insights to enhance your learning.

View Natural Language Processing with Transformers,... on Amazon

Show steps

Read the chapters related to fine-tuning and knowledge distillation.
Experiment with the code examples provided in the book.
Compare the book's approach to the course materials.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Create a Cheat Sheet for Fine-Tuning LLMs

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Consolidate your knowledge by creating a cheat sheet that summarizes the key steps and techniques for fine-tuning LLMs. This will serve as a valuable reference for future projects.

Show steps

Review the course materials and identify the key steps for fine-tuning LLMs.
Summarize each step in a concise and easy-to-understand manner.
Include code snippets and examples where appropriate.
Organize the cheat sheet in a logical and easy-to-navigate format.

Create a Blog Post on Knowledge Distillation

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Reinforce your understanding of knowledge distillation by explaining the concepts in your own words. This will help you identify any gaps in your knowledge and solidify your learning.

Show steps

Research different knowledge distillation techniques.
Summarize the key concepts in a clear and concise manner.
Provide examples of how knowledge distillation can be used in practice.
Publish your blog post online.

Fine-tune BERT for Question Answering

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Apply your knowledge by fine-tuning BERT for a challenging NLP task. This project will solidify your understanding of fine-tuning techniques and the Hugging Face library.

Show steps

Choose a question answering dataset.
Preprocess the data and prepare it for fine-tuning.
Fine-tune a BERT model on the dataset.
Evaluate the model's performance.

Implement Attention Mechanisms from Scratch

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Deepen your understanding of attention mechanisms by implementing them from scratch. This will give you a more intimate understanding of how they work.

Show steps

Study the scaled dot-product attention mechanism.
Implement the attention mechanism using NumPy or PyTorch.
Test your implementation with sample data.

Career center

Learners who complete 2025 Fine Tuning LLM with Hugging Face Transformers for NLP will develop knowledge and skills that may be useful to these careers:

NLP Model Developer

An NLP Model Developer creates and improves natural language processing models for various applications. This course is a strong fit for an NLP Model Developer because it covers the fine-tuning of various large language models, a core skill for this role. This course may help a developer understand knowledge distillation techniques, an important optimization method. The focus on transformer models like BERT, and T5, and the use of Hugging Face Transformers tools are all essential to the day to day work of an NLP Model Developer. The material on building custom applications using these models is also useful.

See salaries and explore the career path for NLP Model Developer

Natural Language Processing Engineer

A Natural Language Processing Engineer focuses on developing and implementing algorithms that enable computers to understand and process human language. This course directly aligns with this career path by delving into transformer models like BERT and T5, and focusing on fine tuning techniques. A Natural Language Processing Engineer will find the course's coverage of practical applications, such as sentiment analysis, named entity recognition, summarization, and model optimization, to be directly applicable. This course may help an aspiring Natural Language Processing Engineer understand how these tools and models work, and how to implement solutions to real problems.

See salaries and explore the career path for Natural Language Processing Engineer

Machine Learning Engineer

A Machine Learning Engineer builds and maintains the infrastructure for machine learning models, and applies these models to solve real-world problems. This course helps build a foundation in fine-tuning large language models, which is a vital skill for any machine learning engineer. The course's focus on BERT, DistilBERT, MobileBERT, and TinyBERT provides a deep understanding of optimizing and applying transformer models, and its emphasis on custom datasets means that you'll be ready to tackle real-world engineering challenges. Sections on training, evaluating and benchmarking the performance of language models using Hugging Face Transformers are also crucial for a machine learning engineer.

See salaries and explore the career path for Machine Learning Engineer

Text Analytics Specialist

A Text Analytics Specialist uses computational techniques to analyze, and interpret text data. This course will help a Text Analytics Specialist by providing a thorough understanding of transformer models and fine tuning techniques using Hugging Face Transformers. The course covers a range of NLP techniques and it will be particularly helpful as it covers practical applications such as sentiment classification, text summarization, named entity recognition, and fake news detection. The work on applying these techniques to custom datasets makes this course very helpful for the role of Text Analytics Specialist.

See salaries and explore the career path for Text Analytics Specialist

Computational Linguist

A Computational Linguist develops computational models of language and uses these tools to analyze and understand human language. This course directly aligns with this role by focusing on the practical aspects of training and fine-tuning transformer models for NLP tasks. The focus on BERT, DistilBERT, MobileBERT, TinyBERT, and T5 models provides a strong foundation in state of the art NLP techniques. Material on text classification, sentiment analysis, summarization and named entity recognition will help a computational linguist develop a more practical understanding of the field. Understanding how to apply Hugging Face to these tasks will assist the computational linguist greatly in their work.

See salaries and explore the career path for Computational Linguist

Chatbot Developer

A Chatbot Developer creates and maintains conversational AI systems by developing the backend models that process text and generate responses. This course will be very useful to a Chatbot Developer. The course's focus on training, fine-tuning and deploying large language models can provide a solid foundation in the building blocks for these systems. The material on building custom chat applications using LLAMA is especially relevant. The knowledge of Hugging Face Transformers is also key to this role. Hands-on experience with the software and specific tasks will help a Chatbot Developer understand all aspects of their role.

See salaries and explore the career path for Chatbot Developer

Artificial Intelligence Researcher

An Artificial Intelligence Researcher explores and develops new algorithms to advance the field of artificial intelligence, often requiring a master's or doctoral degree. This course may be useful for a researcher because it provides an in-depth look at transformer models and their fine-tuning, which can be a great starting point for more advanced research. The course covers the practical application of models such as BERT, DistilBERT, and T5, and also has material on knowledge distillation that may help you pursue advanced studies. It also introduces the Vision Transformer which may be a starting point for image recognition models. The emphasis on the Hugging Face library, a standard tool for AI researchers, can be invaluable.

See salaries and explore the career path for Artificial Intelligence Researcher

Research Scientist

A Research Scientist will investigate and explore new areas in natural language processing. This course will help a Research Scientist by providing a deep understanding of transformer models such as BERT, T5, and others. The detailed exploration of fine-tuning techniques and model optimization through knowledge distillation will be very useful for research. The skills gained from this course, such as the use of Hugging Face Transformers and fine-tuning models with custom data, will help a Research Scientist develop new and innovative solutions to research problems. This course may not be enough to take on a research role directly, but will be useful in preparing for such a role.

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Software Developer

A Software Developer designs and builds software applications and systems to solve specific problems. This course may be useful for Software Developers who wish to specialize in deploying natural language processing models. The course focuses on how to train, fine-tune and optimize models for practical use, particularly the fine-tuning of LLMs using custom datasets and the application of knowledge distillation. The hands-on experience with the Hugging Face library is also be helpful for a Software Developer who is implementing machine learning models. The material on building custom chat applications with LLAMA will also be relevant to this role.

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Data Scientist

A Data Scientist analyzes large datasets, extracts insights, and builds predictive models. This course may be useful for Data Scientists, as it introduces the application of transformer models for text-based data. The course's focus on fine-tuning techniques with BERT and T5 models can be particularly useful, allowing a data scientist to improve their ability to analyze unstructured text data. The course will help a Data Scientist use the Hugging Face Transformers library, which is a vital tool for working with transformer models. The work with custom datasets makes the course even more useful for data science professionals.

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Machine Learning Consultant

A Machine Learning Consultant advises clients on how to implement machine learning solutions to meet their specific business objectives. This course may be useful for a consultant by providing them with a deep understanding of transformer models, particularly around fine-tuning, optimization, and model deployment. Specifically the focus on BERT models and its variants, and the use of Hugging Face Transformers, can help a consultant develop solutions for clients. The experience of working with custom datasets and real world problems such as fake news detection and sentiment analysis is particularly valuable in the consulting world.

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AI Solutions Architect

An AI Solutions Architect is responsible for designing and implementing AI solutions that meet specific business needs. This course may be useful, as it will allow the solutions architect to understand the capabilities and limitations of transformer models. The course's coverage of model optimization, fine-tuning and knowledge distillation will be useful in designing efficient and effective solutions. A solutions architect needs to be up to speed on the practical aspects of model building, and this course can provide good familiarity. The course's application-focused approach and the material on Hugging Face can help the AI Solutions Architect be prepared.

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AI Product Manager

An AI Product Manager is responsible for overseeing the development and execution of products that use artificial intelligence. This course may be useful for an AI Product Manager, as it introduces them to the capabilities and limitations of transformer models, and the effort involved in fine-tuning and optimizing them. The course provides a look under the hood at large language model development, and the use of Hugging Face will give an AI Product Manager a better understanding of what goes into delivering an AI product. The practical application of models for tasks such as sentiment analysis, text summarization, and custom chat applications will also be helpful in this role.

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Data Analyst

A Data Analyst examines data to identify trends and help inform business decisions. This course may be useful for a Data Analyst because it introduces techniques for working with text data, which is often a part of a larger dataset, or a data set all its own. The course's coverage of transformer models and their applications, including sentiment analysis and text summarization, can enhance a Data Analyst's ability to derive insights from textual information. The practical examples of working with Hugging Face Transformers and fine-tuning models on custom data can help a Data Analyst gain familiarity with these tools.

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Machine Translation Specialist

A Machine Translation Specialist focuses on developing, training and fine-tuning models for translating text from one language to another. This course may be useful to a Machine Translation Specialist since it does cover the fine-tuning of transformer models, which is the core of modern machine translation. While this course does not specifically focus on machine translation, it covers the techniques and models that underpin it, and it covers how to train transformers on custom data, which is key to this role. It also provides familiarity with Hugging Face Transformers, which is a standard tool for any machine translation specialist.

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2025 Fine Tuning LLM with Hugging Face Transformers for NLP

What's inside

Learning objectives

Syllabus

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Reviews summary

Practical llm fine-tuning with hugging face

Activities

Career center

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