Generative AI Advance Fine-Tuning for LLMs from Coursera

Fine-tuning a large language model (LLM) is crucial for aligning it with specific business needs, enhancing accuracy, and optimizing its performance. In turn, this gives businesses precise, actionable insights that drive efficiency and innovation. This course gives aspiring gen AI engineers valuable fine-tuning skills employers are actively seeking.

During this course, you’ll explore different approaches to fine-tuning and causal LLMs with human feedback and direct preference. You’ll look at LLMs as policies for probability distributions for generating responses and the concepts of instruction-tuning with Hugging Face. You’ll learn to calculate rewards using human feedback and reward modeling with Hugging Face. Plus, you’ll explore reinforcement learning from human feedback (RLHF), proximal policy optimization (PPO) and PPO Trainer, and optimal solutions for direct preference optimization (DPO) problems.

As you learn, you’ll get valuable hands-on experience in online labs where you’ll work on reward modeling, PPO, and DPO.

If you’re looking to add in-demand capabilities in fine-tuning LLMs to your resume, ENROLL TODAY and build the job-ready skills employers are looking for in just two weeks!

What's inside

Syllabus

Different Approaches to Fine-Tuning

In this module, you’ll begin by defining instruction-tuning and its process. You’ll also gain insights into loading a dataset, generating text pipelines, and training arguments. Further, you’ll delve into reward modeling, where you’ll preprocess the dataset and apply low-rank adaptation (LoRA) configuration. You’ll also learn to quantify quality responses, guide model optimization, and incorporate reward preferences. You’ll also describe reward trainer, an advanced training technique to train a model, and reward model loss using Hugging Face. The labs, in this module will allow practice on instruction-tuning and reward models.

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Develops skills in fine-tuning LLMs, which are actively sought by employers looking to enhance accuracy and optimize performance for specific business needs

Explores reinforcement learning from human feedback (RLHF), proximal policy optimization (PPO), and direct preference optimization (DPO), which are cutting-edge techniques in the field

Provides hands-on experience in online labs focused on reward modeling, PPO, and DPO, allowing learners to apply theoretical knowledge to practical scenarios

Uses Hugging Face, a popular library in the field, for instruction-tuning, reward modeling, and sentiment analysis, providing learners with industry-relevant tools

Requires familiarity with methods like PPO and reinforcement learning, which could be considered subjects of study on their own, so learners may need to supplement their knowledge

Reviews summary

Hands-on advanced llm fine-tuning

According to learners, this course offers an in-depth look at advanced techniques for fine-tuning Large Language Models, specifically covering Instruction-Tuning, Reward Modeling, PPO, and DPO methods using the Hugging Face library. Many students appreciated the focus on practical application through the hands-on labs, which are seen as valuable for developing job-ready skills in this field. The content is considered relevant and cutting-edge. However, multiple reviews indicate the course is quite challenging and moves at a very fast pace, strongly recommending that participants possess a solid background in deep learning and Python beforehand. It is generally considered ideal for advanced learners rather than intermediate ones. Some learners also reported minor issues with the online lab environment.

Practical implementation using Hugging Face.

"Appreciated learning how to implement these advanced techniques using the Hugging Face ecosystem."

"The course effectively integrates the Hugging Face Transformers library for practical examples."

"Using Hugging Face trainers made the complex fine-tuning process more manageable."

Covers highly relevant job skills.

"The skills taught are directly applicable to current AI/ML engineering roles."

"Great course for updating my resume with in-demand generative AI skills."

"Content is very relevant to the current state of LLM development and fine-tuning."

Valuable hands-on experience with techniques.

"The hands-on labs were the most beneficial part for me, applying PPO and DPO concepts directly."

"Getting to work with the code and see the models in action in the labs was great."

"Labs helped solidify the theoretical concepts taught in the lectures."

Covers cutting-edge fine-tuning methods.

"Covers very advanced topics like RLHF, PPO, and DPO which are crucial for aligning LLMs with human preferences."

"I learned about instruction-tuning and reward modeling in a structured way using Hugging Face."

"Provides a good conceptual understanding of complex methods like Proximal Policy Optimization (PPO) for fine-tuning."

Some users experienced technical issues.

"Experienced some technical glitches and slowness with the lab environment."

"Setting up and running the labs sometimes required troubleshooting outside the course material."

"Minor issues with lab instances interrupting the flow."

Content moves very quickly.

"The pace is incredibly fast, covering complex topics rapidly."

"It felt rushed at times, especially when explaining the math behind methods like PPO."

"Had to pause and re-watch lectures frequently because of the speed."

Not for beginners, needs ML/Python background.

"This course assumes significant prior knowledge in deep learning, transformers, and Python."

"Moves very quickly; if you aren't already solid on ML fundamentals, you might struggle."

"Definitely requires a strong background. It's truly an 'advanced' course."

"I felt lost in some sections without a deeper understanding of PyTorch and training loops."

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 Generative AI Advance Fine-Tuning for LLMs with these activities:

Review Transformer Architecture

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Review the fundamentals of transformer architecture to better understand the underlying mechanisms of LLMs.

Browse courses on Transformer Architecture

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Read blog posts or articles explaining the transformer architecture.
Watch videos that visually explain the attention mechanism.
Review the original Transformer paper 'Attention is All You Need'.

Read 'Hugging Face Transformers'

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Read this book to gain a deeper understanding of the Hugging Face Transformers library, which is essential for fine-tuning LLMs.

View Putting Knowledge to Work on Amazon

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Read the chapters related to fine-tuning and training.
Experiment with the code examples provided in the book.
Try to adapt the examples to different datasets.

Implement LoRA on a small dataset

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Practice implementing Low-Rank Adaptation (LoRA) to solidify your understanding of parameter-efficient fine-tuning.

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Choose a small, publicly available dataset.
Implement LoRA using the Hugging Face Transformers library.
Compare the performance of LoRA with full fine-tuning.

Four other activities

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Follow Hugging Face RLHF Tutorial

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Follow a step-by-step tutorial on Reinforcement Learning from Human Feedback (RLHF) to gain practical experience.

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Find a comprehensive RLHF tutorial on the Hugging Face website.
Carefully follow each step of the tutorial, paying attention to the code.
Experiment with different hyperparameters to see their effect.

Fine-tune an LLM for a specific task

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Start a project to fine-tune an LLM for a specific task, such as text summarization or question answering.

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Choose a specific task and find a relevant dataset.
Fine-tune an LLM using the techniques learned in the course.
Evaluate the performance of the fine-tuned model.
Document your project and share your findings.

Read 'Deep Reinforcement Learning Hands-On'

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Read this book to gain a deeper understanding of reinforcement learning algorithms, particularly PPO, which is used in RLHF.

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

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Read the chapters related to policy gradient methods and PPO.
Understand the mathematical foundations of PPO.
Implement PPO from scratch or using a library like OpenAI Baselines.

Write a blog post on DPO

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Write a blog post explaining Direct Preference Optimization (DPO) to solidify your understanding and share your knowledge.

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Research DPO and its advantages over other methods.
Write a clear and concise explanation of DPO.
Include examples and diagrams to illustrate the concepts.
Publish your blog post on a platform like Medium or your personal website.

Career center

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

Generative AI Engineer

A Generative AI Engineer designs, develops, and implements AI models, with a focus on large language models. This role requires a strong understanding of fine-tuning methods, especially when using human feedback. This course in generative AI fine-tuning is an ideal starting point for this career. The course covers instruction-tuning and reward modeling, providing crucial skills for adjusting model behaviors. It emphasizes practical labs with PPO and DPO. These approaches are integral to training AI models to align with specific business requirements. The course's focus on direct preference optimization is especially helpful for enhancing the engineer's ability to refine generative models based on human preference. A generative AI engineer should seek out opportunities to practice these techniques.

See salaries and explore the career path for Generative AI Engineer

Machine Learning Engineer

A Machine Learning Engineer develops and deploys AI models, especially those that leverage deep learning and neural networks. This role is responsible for model improvement and performance optimization. This course's emphasis on fine-tuning large language models (LLMs) helps an engineer achieve better model outcomes. This includes instruction-tuning, reward modeling, and using PPO and DPO techniques. Understanding how to train models based on human feedback, as covered by this course, is essential for developing models that meet real-world needs. The course labs provide hands-on experience with techniques useful for a machine learning engineer.

See salaries and explore the career path for Machine Learning Engineer

Chatbot Developer

A Chatbot Developer creates and maintains conversational AI systems. This role needs a strong understanding of how large language models function and how to fine-tune them for better user interaction. This course directly addresses this need by teaching instruction-tuning, reward modeling, and direct preference optimization. The hands-on experience with PPO and DPO provided in this course is especially valuable for refining chatbot responses. A chatbot developer benefits from a deep knowledge of generative AI. This course helps develop those skills.

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Natural Language Processing Engineer

A Natural Language Processing Engineer focuses on developing applications that allow computers to understand, interpret, and generate human language. This role requires a strong grasp of techniques in fine-tuning and optimizing large language models. This course helps build a foundation for NLP engineering, as it covers these topics in depth. It provides hands-on experience with instruction-tuning, reward modeling, and PPO and DPO methods. An NLP engineer will benefit from the hands-on work using Hugging Face, which is an important tool for designing language-based applications. Understanding the nuances of adjusting model parameters based on desired responses is important to this job.

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

Computational Linguist

A Computational Linguist develops algorithms and models to process and analyze human language. This role requires expertise in natural language processing techniques. This course offers a deep dive into the fine-tuning of large language models and is a strong fit for a computational linguist. The course covers instruction-tuning, reward modeling, and how to implement PPO and DPO. These are essential tools for training models to understand and generate language more accurately. The practical labs in the course will allow a linguist to integrate methods into their workflow.

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AI Research Scientist

An AI Research Scientist explores new methodologies and algorithms for AI. This role often requires a deep understanding of the theoretical underpinnings of different machine learning techniques. This course introduces concepts like reinforcement learning from human feedback and direct preference optimization, which are relevant to this field. The course's focus on instruction-tuning, reward modeling, and hands-on experience with PPO and DPO helps the research scientist implement and assess new approaches to generate better results. This course helps build a foundation for a research scientist to understand the practical applications of these ideas. Most AI research scientist roles require a PhD.

See salaries and explore the career path for AI Research Scientist

AI Solutions Architect

An AI Solutions Architect designs and oversees the implementation of AI solutions for businesses. This role requires a broad understanding of AI capabilities and how to apply them to solve specific business needs. This course's focus on fine-tuning LLMs using human feedback and direct preference optimization provides the architect with valuable insight for developing customized solutions for clients. A solutions architect benefits from the course's discussion of instruction-tuning and reward modeling and also benefits from the labs on PPO and DPO. An AI solutions architect who has a strong grasp of these methods is well-positioned to develop compelling solutions.

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Data Scientist

A Data Scientist analyzes data to extract meaningful insights and build predictive models. While this role is broad, familiarity with language models and their tuning is useful, especially when working with text data. This course may help a data scientist who wishes to specialize in working with unstructured data. The course covers reward modeling and direct preference optimization, which are critical for refining model outputs based on business needs. The labs offer hands-on skills. This helps create a foundation for a data scientist to tackle a wide range of problems. The course also provides an introduction to the complexities of fine-tuning generative AI models.

See salaries and explore the career path for Data Scientist

AI Product Manager

An AI Product Manager defines the strategy and roadmap for AI-driven products. This role involves understanding the technical aspects of AI and how they can be applied to create business value. While this role is less hands-on, the course provides an AI product manager with a technical understanding of generative AI models and their fine tuning. A product manager who understands instruction tuning, reward modeling, and the concepts of PPO and DPO will be better able to assess the capabilities of their product offerings and how they can provide value. The product manager may also be able to better guide the engineering teams.

See salaries and explore the career path for AI Product Manager

Robotics Software Engineer

A Robotics Software Engineer develops software to control and interact with robots. While this field may seem distant, the skills in this course apply to training robots with AI. The focus on fine-tuning large language models with human feedback and direct preference optimization is relevant to robotic control systems. The course offers an introduction to reward modeling, which is useful for training robots to reach specific goals. The hands-on practice with PPO and DPO provides a software engineer with concrete strategies to improve robotics control systems. AI is a key component of modern robotics. This course may be useful to a robotics engineer.

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Software Developer

A Software Developer designs and builds software applications. While this role is broad, the course can provide valuable skills in the rapidly evolving domain of AI-powered applications and how to integrate these language capabilities into tools. This course introduces key concepts in fine-tuning LLMs with instruction-tuning and reward modeling, and explores PPO and DPO methodologies. A software developer may find the practical aspects of this course useful. A software developer with the skills obtained in the course will be better able to take advantage of the latest in AI development.

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AI Ethicist

An AI Ethicist examines the ethical implications of AI technologies. While this role is more focused on social impact, understanding the technical details of how models are trained and deployed helps with this. This course may be useful for training models based on human feedback, and this helps an ethicist understand if bias or other ethical pitfalls are present. A knowledge of areas such as instruction-tuning, PPO and direct preference optimization is useful when evaluating an AI system. An AI ethicist can use this knowledge to advocate for better implementation of systems.

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Data Analyst

A Data Analyst examines data to identify trends and insights, often providing reports to support business decisions. While not directly aligned with generative AI, there may be some situations where this course in fine-tuning LLMs may come in handy, for example when analyzing large volumes of text data. A data analyst may find the course's treatment of instruction-tuning and reward modeling tangentially useful. The hands-on experience may help them understand how models can be refined to extract specific information. This course may be useful if a data analyst wants to explore the capabilities of AI in their workflow.

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Bioinformatician

A Bioinformatician applies computational techniques to biological data. This role uses tools from machine learning, and while this role does not directly benefit from the course, there may be specific tasks that require a fine tuning of models. This course may be useful in specific situations, but is not directly applicable to the bulk of this career. The course does cover topics such as rewards modeling, and exploration of PPO and DPO and these topics may come in handy depending on the nature of the specific research project. The fine-tuning skills learned in the course might come in handy for a bioinformatician.

See salaries and explore the career path for Bioinformatician

Technical Writer

A Technical Writer creates documentation for technical products and services. This role does not need to directly implement AI technologies, but understanding the fundamentals of algorithms is helpful in describing a project or product to a user. This course may be useful to help a technical writer learn about instruction-tuning and reinforcement learning. It can help this writer describe processes in an informative manner. A technical writer may benefit from learning about the practical implications of LLMs and how they can be refined.

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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 Generative AI Advance Fine-Tuning for LLMs .

Putting Knowledge to Work

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Provides a comprehensive guide to using the Hugging Face Transformers library. It covers various transformer models and their applications, including fine-tuning. It valuable resource for understanding how to implement the concepts taught in the course. This book is commonly used by both industry professionals and academic researchers.

Putting Knowledge to Work

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Putting Knowledge to Work

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Deep Reinforcement Learning Hands-On

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Provides a practical introduction to deep reinforcement learning. It covers various algorithms, including Proximal Policy Optimization (PPO). While the course provides an overview of PPO, this book offers a more in-depth explanation and practical examples. This book is valuable as additional reading for those who want to delve deeper into RLHF.

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

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Generative AI Advance Fine-Tuning for LLMs

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What's inside

Syllabus

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Reviews summary

Hands-on advanced llm fine-tuning

Activities

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