Mastering Generative AI: Advanced Fine-Tuning for LLMs from edX

Employers are actively hunting for AI engineers who know how to fine-tune transformers for gen AI applications. This Mastering Generative AI - Advanced Fine-Tuning for LLMs course is designed to give AI engineers and other AI specialists the highly sought-after skills employers need.

AI engineers use advanced fine-tuning skills for LLMs to tailor pre-trained models for specific tasks to ensure accuracy and relevance in applications like chatbots, translation, and content generation.

During this course, you’ll explore the basics of instruction-tuning with Hugging Face, reward modeling, and training a reward model. You’ll look at proximal policy optimization (PPO) with Hugging Face and its configuration, large language models (LLMs) as distributions, and reinforcement learning from human feedback (RLHF). Plus, you’ll investigate direct performance optimization (DPO) with Hugging Face using the partition function.

As you progress through the course, you’ll also build your practical hands-on experience in online labs where you’ll work on reward modeling, PPO, and DPO.

If you’re keen to extend your gen AI engineering skills to include advanced fine-tuning for LLMs so you can catch the eye of an employer, ENROLL TODAY and power up your resume in just 2 weeks!

Prerequisites: To take this course, you need knowledge of LLMs, instruction-tuning, and reinforcement learning. Familiarity with machine learning and neural network concepts is useful too.

What's inside

Learning objectives

Advanced, job-ready skills in fine-tuning for llms employers are looking for in just 2 weeks.
How to perform instruction-tuning and reward modeling with the hugging face.
How to use large language models (llms) as policies and reinforcement learning with human feedback (rlhf).

How to apply direct preference optimization (dpo) with partition function and hugging face and create an optimal solution to a dpo problem.
How to use proximal policy optimization (ppo) with hugging face to create a scoring function and perform dataset tokenization.

Advanced, job-ready skills in fine-tuning for llms employers are looking for in just 2 weeks.
How to perform instruction-tuning and reward modeling with the hugging face.
How to use large language models (llms) as policies and reinforcement learning with human feedback (rlhf).
How to apply direct preference optimization (dpo) with partition function and hugging face and create an optimal solution to a dpo problem.
How to use proximal policy optimization (ppo) with hugging face to create a scoring function and perform dataset tokenization.

Syllabus

Module 0: Welcome

Video: Course Introduction

Reading: Professional Certificate Overview

Reading: General Information

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Teaches advanced fine-tuning skills for LLMs, which are highly sought after by employers in the field of artificial intelligence and machine learning

Uses Hugging Face, a popular open-source library for machine learning, making it easier for learners to apply their skills in real-world projects

Explores proximal policy optimization (PPO) and direct performance optimization (DPO), which are state-of-the-art techniques in reinforcement learning

Requires knowledge of LLMs, instruction-tuning, and reinforcement learning, so learners should come prepared with a solid foundation in these areas

Includes hands-on labs for reward modeling, PPO, and DPO, allowing learners to gain practical experience with these advanced fine-tuning techniques

Presented by IBM, a company recognized for its contributions to artificial intelligence and its development of cutting-edge AI technologies and platforms

Reviews summary

Advanced fine-tuning skills for llms

According to the course description and structure, learners can expect to gain job-ready skills in advanced fine-tuning for LLMs, focusing on practical techniques using Hugging Face. The syllabus features intensive hands-on labs covering key methods like PPO and DPO, which are critical for applying theoretical knowledge. Note that the course is fast-paced, designed to be completed in just two weeks, and requires strong prerequisites in LLMs, RL, and machine learning to succeed. The content appears highly relevant for current AI engineering roles.

Deep dive into PPO and DPO techniques.

"The course covers advanced methods like PPO and DPO, which is what I needed."

"Getting into RLHF and DPO was the main reason I considered taking this."

"It's good that it focuses on these advanced fine-tuning strategies for LLMs."

Focuses on skills employers are seeking.

"The course objectives align well with skills needed for AI engineering roles."

"Looks like the topics are directly relevant to industry jobs in generative AI."

"Seems designed to provide skills attractive to employers looking for LLM specialists."

Hands-on labs reinforce concepts.

"The labs seem crucial for applying the theory from the videos."

"Working through the PPO and DPO labs looks like it will help solidify understanding."

"I expect the hands-on exercises using Hugging Face will be very practical."

Intensive 2-week schedule covers much material.

"Fitting all this into two weeks seems very ambitious."

"The pace looks challenging, requiring significant time commitment."

"I anticipate the 2-week structure will be demanding for many learners."

Requires solid prior knowledge of LLMs, RL.

"Looks like you really need to know LLMs and RL beforehand to keep up."

"The prerequisites mentioned are likely very important for success in this course."

"I assume prior knowledge of ML/neural nets is truly necessary before enrolling."

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 Mastering Generative AI: Advanced Fine-Tuning for LLMs with these activities:

Review Reinforcement Learning Fundamentals

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Solidify your understanding of reinforcement learning concepts, which are crucial for understanding RLHF and PPO.

Browse courses on Reinforcement Learning

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Review key concepts like Markov Decision Processes and reward functions.
Work through examples of Q-learning and policy gradient methods.
Summarize the differences between model-free and model-based RL.

Read 'Reinforcement Learning: An Introduction' by Sutton and Barto

Show steps

Gain a deeper understanding of reinforcement learning principles, which are essential for mastering RLHF and PPO.

View Reinforcement Learning, second edition: An... on Amazon

Show steps

Read the chapters on Markov Decision Processes and Dynamic Programming.
Study the sections on Monte Carlo methods and Temporal Difference learning.
Review the chapters on policy gradient methods and actor-critic algorithms.

Implement Instruction-Tuning on a Small Dataset

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Reinforce your understanding of instruction-tuning by applying it to a small, manageable dataset.

Show steps

Select a small dataset suitable for instruction-tuning (e.g., a question-answering dataset).
Prepare the dataset in the format required for instruction-tuning.
Fine-tune a pre-trained LLM on the prepared dataset using Hugging Face Transformers.
Evaluate the performance of the fine-tuned model on a held-out test set.

Four other activities

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Show all seven activities

Blog Post: Comparing Fine-Tuning Approaches

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Solidify your knowledge by explaining the different fine-tuning approaches (instruction-tuning, PPO, DPO) in a clear and concise manner.

Show steps

Research the different fine-tuning approaches covered in the course.
Outline the key differences and trade-offs between these approaches.
Write a blog post explaining each approach and comparing their strengths and weaknesses.
Include examples of when each approach is most suitable.

Fine-Tune an LLM for a Specific Task

Show steps

Apply your knowledge to a real-world problem by fine-tuning an LLM for a specific task of your choice.

Show steps

Choose a specific task for which you want to fine-tune an LLM (e.g., text summarization, code generation).
Gather or create a dataset suitable for fine-tuning on the chosen task.
Experiment with different fine-tuning techniques (instruction-tuning, PPO, DPO) and hyperparameters.
Evaluate the performance of the fine-tuned model and compare it to a baseline model.
Document your project, including the task, dataset, fine-tuning techniques, and results.

Read 'Deep Reinforcement Learning Hands-On' by Maxim Lapan

Show steps

Gain practical experience with deep reinforcement learning algorithms, including PPO, through hands-on examples.

View Deep Reinforcement Learning Hands-On: A... on Amazon

Show steps

Read the chapters on policy gradient methods and actor-critic algorithms.
Study the examples of implementing PPO in PyTorch.
Experiment with different hyperparameters and network architectures.

Contribute to a Hugging Face Transformers Project

Show steps

Deepen your understanding of LLMs and fine-tuning by contributing to an open-source project related to Hugging Face Transformers.

Show steps

Explore the Hugging Face Transformers repository on GitHub.
Identify an issue or feature that you can contribute to (e.g., bug fix, documentation improvement, new example).
Implement your contribution and submit a pull request.
Participate in code review and address any feedback from the maintainers.

Career center

Learners who complete Mastering Generative AI: Advanced Fine-Tuning for LLMs will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A machine learning engineer builds and deploys machine learning models, and this course on advanced fine-tuning for large language models helps prepare for this work. The role involves customizing pre-trained models for specific applications, like chatbots or content generation, which is directly addressed by the course's focus on instruction tuning, reward modeling, and reinforcement learning from human feedback. A machine learning engineer will be involved in the model optimization process, and this course provides hands-on experience with techniques such as proximal policy optimization and direct preference optimization. Anyone pursuing the path of a machine learning engineer should consider this course to build practical and in-demand skills.

See salaries and explore the career path for Machine Learning Engineer

Artificial Intelligence Engineer

An artificial intelligence engineer develops and implements AI solutions, and this course provides valuable skills for this role. The course covers methods for fine-tuning large language models for specific tasks, such as text translation or code generation, and AI engineers must understand how to do this. The emphasis on instruction tuning, reward modeling, proximal policy optimization, and direct preference optimization makes this training directly relevant. An artificial intelligence engineer can use the hands-on lab experience with these techniques to hone their craft. An aspiring artificial intelligence engineer should take this course as it teaches the highly sought-after skills that employers are looking for.

See salaries and explore the career path for Artificial Intelligence Engineer

Natural Language Processing Engineer

A natural language processing engineer develops systems that enable computers to understand and process human language, and this course in advanced fine-tuning helps build the capacity for this practice. The course’s focus on large language models and fine-tuning techniques directly relates to creating the complex models used in NLP applications. With instruction tuning, reinforcement learning, and optimization techniques covered in the course, a natural language processing engineer can use these tools to improve the accuracy and relevance of language processing models. The practical application of these techniques using Hugging Face and in online labs will deepen the expertise of a natural language processing engineer. Anyone who strives to become a natural language processing engineer can enhance their skills with this course.

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

Generative AI Specialist

A generative AI specialist works with models that generate various types of content like text, images, or code. This course in advanced fine-tuning for large language models is extremely relevant for a generative AI specialist. The course content on instruction tuning, reward modeling, reinforcement learning, and direct preference optimization are critical to customizing generative AI models for specific tasks. A generative AI specialist will make extensive use of the Hugging Face tools that are covered in the course. Generative AI specialists should take this course to work with advanced fine-tuning techniques.

See salaries and explore the career path for Generative AI Specialist

AI Research Scientist

An AI research scientist focuses on advancing the capabilities of artificial intelligence, and this course supports the work by teaching advanced fine-tuning for large language models. The course content provides a solid foundation in fine-tuning techniques, such as instruction tuning, reward modeling, proximal policy optimization, and direct preference optimization, which are essential for improving the performance of AI models. An AI research scientist will benefit from hands-on experience with tools like Hugging Face and the practical lab work offered. Anyone seeking to become an AI research scientist should consider this course as part of their development.

See salaries and explore the career path for AI Research Scientist

Machine Learning Researcher

A machine learning researcher develops new machine learning algorithms and techniques, and this course can be helpful to provide a deeper understanding of fine-tuning for large language models. The course focuses on advanced fine-tuning methods such as instruction tuning, reward modeling, proximal policy optimization, and direct preference optimization, which are important areas of study. A machine learning researcher can use the practical experience gained from the course labs to contribute to projects. Anyone pursuing a career as a machine learning researcher can gain a solid perspective from this course.

See salaries and explore the career path for Machine Learning Researcher

Research Engineer

A research engineer works on the development of new technologies and processes, and this course on fine-tuning for large language models may be relevant. The course content, which covers instruction tuning, reward modeling, and reinforcement learning, offers a solid ground in methods for improving the performance of AI models. A research engineer may use the practical skills obtained from hands-on lab training with Hugging Face to contribute to AI-related projects. A research engineer involved in AI projects should consider taking this course.

See salaries and explore the career path for Research Engineer

Applied Scientist

An applied scientist uses scientific principles to solve practical problems, and this course may be beneficial for those working with AI applications. The course addresses methods for advanced fine-tuning of large language models, which offers a foundation in how to customize models for particular tasks. An applied scientist can use the skills in the course to enhance AI systems for better accuracy and relevance. Instruction tuning, optimization techniques, and the practical experience with Hugging Face can deepen their knowledge in the field. An applied scientist working on AI projects can enhance their tools with this course.

See salaries and explore the career path for Applied Scientist

Data Scientist

A data scientist analyzes data to extract meaningful insights, and this course in advanced fine-tuning for large language models can help enhance their capabilities with AI. Although this role may entail more general data analysis, the skills gained in fine-tuning models can be applied to improve AI-powered data analysis tools. The course’s focus on instruction tuning, reward modeling, reinforcement learning from human feedback, and optimization techniques can be useful to a data scientist. The hands-on experience with Hugging Face makes this course valuable to a data scientist seeking to expand their skillset into artificial intelligence. Those who pursue a career in data science and want to explore AI will benefit from this course.

See salaries and explore the career path for Data Scientist

Computational Linguist

A computational linguist develops computational models of language, and this course can be highly relevant by focusing on methods for fine-tuning large language models. The course’s emphasis on techniques like instruction tuning, reward modeling, and reinforcement learning from human feedback are applicable to building sophisticated language models. A computational linguist, who uses tools like Hugging Face, can benefit from the practical training in this course. Those interested in moving into computational linguistics should consider this course.

See salaries and explore the career path for Computational Linguist

AI Consultant

An AI consultant advises businesses on how to implement AI solutions. An understanding of advanced fine-tuning for large language models is beneficial for anyone providing this service. The course covers instruction tuning, reward modeling, and reinforcement learning, which are necessary to understand and recommend AI solutions. An AI consultant can provide better guidance by learning the practical application of these techniques using Hugging Face. Anyone who wants to start a career as an AI consultant can improve their expertise with this course.

See salaries and explore the career path for AI Consultant

AI Product Manager

An AI product manager oversees the development and launch of AI-based products, and this course may be useful to anyone in this role. This course emphasizes advanced fine-tuning techniques for large language models. AI product managers need to understand the technical capabilities of these models in order to make informed product decisions. Familiarity with instruction tuning, reward modeling, and reinforcement learning from human feedback are important for this role. This course helps an AI product manager gain a deeper understanding of AI model optimization and how it impacts product performance. Anyone striving for a career in AI product management may find this course useful.

See salaries and explore the career path for AI Product Manager

Robotics Engineer

A robotics engineer designs, builds, and maintains robots, and this course may be useful in their work, as AI is increasingly integrated into robotics. The course in advanced fine-tuning for large language models can help adapt AI models to control or understand robotic interactions. The instruction tuning, and optimization techniques covered in the course could be valuable for a robotics engineer to develop smarter robots that adapt to dynamic environments and human instructions. A robotics engineer can use practical knowledge gained from hands-on lab experience that explores tools like Hugging Face. This course can be useful to a robotics engineer who wishes to expand their understanding of AI.

See salaries and explore the career path for Robotics Engineer

Data Analyst

A data analyst interprets data and creates reports, and this course may help expand their skillset into AI applications. While this role primarily focuses on data analysis, this course provides a great introduction to the use of AI models for processing data. With instruction tuning, reward modeling, and optimization techniques covered, a data analyst will gain insight into how models can improve accuracy and efficiency. The hands-on experience with tools like Hugging Face can deepen their knowledge of applied machine learning. This course may help a data analyst understand AI more deeply.

See salaries and explore the career path for Data Analyst

Software Developer

A software developer creates software applications, and this course may be useful by providing insight into the growing prevalence of AI in software. The skills in advanced fine-tuning for large language models allow a software developer to understand and integrate AI features into their work. A software developer may need to integrate services using AI models. The practical experience with Hugging Face and the techniques covered in the course may be helpful for a software developer to expand their skillset into AI. Those in software development who wish to extend their knowledge into AI may find this course useful.

See salaries and explore the career path for Software Developer

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Mastering Generative AI: Advanced Fine-Tuning for LLMs.

Reinforcement Learning, second edition

Save

Comprehensive introduction to reinforcement learning, covering fundamental concepts and algorithms. It provides a strong theoretical foundation for understanding RLHF and PPO. It is commonly used as a textbook in university courses. Reading this book will give you a deeper understanding of the underlying principles behind the fine-tuning techniques covered in the course.

Reinforcement Learning, second edition: An...

Hardcover

$$$

Reinforcement Learning, second edition: An...

Kindle Edition

$$$

Deep Reinforcement Learning Hands-On

Save

Provides a practical guide to deep reinforcement learning, with hands-on examples and code implementations. It covers various DRL algorithms, including PPO, and demonstrates how to apply them to real-world problems. This book is more valuable as additional reading than it is as a current reference. It will help you bridge the gap between theory and practice and build your skills in implementing DRL solutions.

Deep Reinforcement Learning Hands-On: A practical...

Kindle Edition

Mastering Generative AI

Advanced Fine-Tuning for LLMs

What's inside

Learning objectives

Syllabus

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Reviews summary

Advanced fine-tuning skills for llms

Activities

Career center

Reading list

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