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LLM Fine Tuning Fundamentals + Fine tune OpenAI GPT model

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Based on 102 ratings
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J Garg - Real Time Learning

"Large Language Models (LLMs) have revolutionized the AI industry, providing unprecedented precision, expanding the possibilities of artificial intelligence. However the pre-trained LLMs may not always meet the specific requirements of an organization, hence there is always a need to Fine tune the LLMs to tailor these models to your unique tasks and requirements."

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"Large Language Models (LLMs) have revolutionized the AI industry, providing unprecedented precision, expanding the possibilities of artificial intelligence. However the pre-trained LLMs may not always meet the specific requirements of an organization, hence there is always a need to Fine tune the LLMs to tailor these models to your unique tasks and requirements."

This comprehensive course is designed to equip you with the skills of LLM Fine tuning technique. It starts with a thorough introduction to the fundamentals of fine tuning while highlighting its critical role to make the LLM models adapt to your specific data. Then we will dive into Hands-on sessions covering the entire Fine-tuning workflow to Fine tune OpenAI GPT model. Through practical sessions, you'll step-by-step learn to prepare & format datasets, execute OpenAI fine tuning processes, and evaluate the model outcomes.

By the end of this course, you will be proficient in Fine tuning the OpenAI's GPT model to meet specific organizational needs and start your career journey as LLM Fine tuning engineer.

What in nutshell is included in the course ?

[Theory]

  • We'll start with LLM and LLM Fine tuning's core basics and fundamentals.

  • Discuss Why is Fine tuning needed, How it works, Workflow of Fine tuning and the Steps involved in it.

  • Different types of LLM Fine tuning techniques including

  • Best practices for LLM Fine tuning.

[Practicals]

  • Get a detailed walkthrough of OpenAI Dashboard and Playground to get a holistic understanding of wide range of tools that OpenAI offers for Generative AI.

  • Follow the OpenAI Fine tuning workflow in practical sessions covering Exploratory Data Analysis (EDA), Data preprocessing, Data formatting, Creating fine tuning job, Evaluation.

  • Understand the OpenAI specialized JSONL format that it accepts for training & test data, and learn about 3 important roles - System, User, Assistant.

  • Calculate the Token count and Fine tuning cost in advance using Tiktoken library.

  • Gain Hands-on experience in fine tuning OpenAI's GPT model on a custom dataset using Python through step-by-step practical sessions.

  • Assess the accuracy and performance of the fine-tuned model compared to the base pre-trained model to evaluate the impact of fine-tuning.

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What's inside

Syllabus

In the section students will get basic idea of LLMs in general. I have discussed about about LLMs, a bit of LLM inner workings, LLM applications etc
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In this video you will get a basic idea of What are Large Language models (LLM) and the nitty gritty things surrounding it.

Why LLMs are so important and what are its various applications.

Pre-trained LLM models are the LLM models that have already been trained on a large dataset mostly for the general tasks. They have already learnt patterns and features which can be then reused for tasks of similar nature

LLM Fine tuning in technical terms is the process of adjusting the parameters of a pre-trained model to make it better suited for a specific task of your personal use case. Fine tuning is about making some tweaks in the already pre-trained LLM model that does general-purpose tasks to make it do more specialized tasks.

In this video, you will learn about the steps involved in the LLM fine tuning process.

What are different types of LLM fine tuning

  • Standard LLM Fine tuning

  • Sequential LLM Fine tuning

  • Layer wise LLM finetuning

  • Feature extraction LLM finetuning

  • RLHF finetuning

  • PEFT finetuning

  • LoRA

  • QLoRA

  • Instruction fine tuning

LLM fine tuning fundamentals quiz

In this video you will get a walkthrough of what dashboard OpenAI offers for developers for Generative AI. You will find the fine tune option in this dashboard.

In this video, I did the basic setup required for LLM fine tuning.

In this video I have shown the roadmap of the steps involved in Fine tuning OpenAI GPT model

In this video, I have performed basic EDA on the input dataset collected for OpenAI LLM fine tuning. EDA includes removing the duplicated and null values.

In this video I have created a world cloud on the collected data for OpenAI LLM fine tuning.

Fine-tuning OpenAI GPT models is directly proportional to the input token count so it is necessary to count the tokens in your input dataset before submitting a fine tuning job in OpenAI

In this video I am splitting the input file to test and train splits.

JSONL or Json lines is same as JSON format except newline characters are used to delimit JSONL data. OpenAI takes only JSONL formatted data for fine tuning.

OpenAI JSONL format contains messages with 3 roles - user, system and assistant role

Convert the train and test csv files into OpenAI accepted format for fine-tuning.

In this video, we will upload the processed files (OpenAI JSONL format) to the OpenAI servers. The purpose of the upload shall be set to fine-tune.

What are the parameters of client.fine_tuning.jobs.create() method of OpenAI.

Create and submit a fine tuning job to openai to fine tune the GPT LLM

In this video, I am finally creating and submitting the job to fine tune OpenAI LLM GPT 3.5 turbo model

It is important to evaluate the fine tuned LLM model for its accuracy from base model and then based on the evaluation results check if the fine tuned LLM model actually performed well than the base pre-trained LLM model.

OpenAI fine tuning Quiz

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Provides hands-on experience fine-tuning OpenAI's GPT model, which is a highly sought-after skill in the field of applied artificial intelligence
Covers the OpenAI specialized JSONL format, which is essential for preparing data for fine-tuning and understanding the roles of system, user, and assistant
Explores various LLM fine-tuning techniques, including standard, sequential, layer-wise, feature extraction, RLHF, PEFT, LoRA, QLoRA, and instruction fine-tuning, offering a comprehensive overview
Requires familiarity with Python and basic data manipulation techniques, which may pose a barrier for learners without a programming background
Focuses specifically on fine-tuning OpenAI's GPT model, which may limit its applicability to other LLMs or platforms, but allows for deep expertise
Teaches how to calculate token count using the Tiktoken library, which is crucial for estimating fine-tuning costs in advance and managing resource allocation

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Reviews summary

Practical openai gpt fine tuning

According to learners, this course offers a practical, hands-on approach to LLM fine-tuning, specifically focusing on OpenAI GPT models. Many find the step-by-step workflow for fine-tuning with the OpenAI API particularly clear and useful for getting started. While it provides a solid foundation, some suggest that the theoretical sections could be more in-depth and wish it covered other fine-tuning techniques beyond OpenAI's specific method. Learners appreciate the focus on practical implementation details like data formatting (JSONL) and token calculation. Overall, it's seen as a valuable resource for applying fine-tuning with OpenAI, though keeping up with API changes can sometimes be a challenge.
Highlights costs associated with OpenAI fine-tuning.
"Remember that fine-tuning with OpenAI costs money, which isn't always highlighted enough for beginners."
"The course shows you how, but be mindful that the actual fine-tuning jobs incur costs."
"Good to know about token costs before starting the practicals."
"Calculating token count was a useful part, especially for managing potential costs."
Step-by-step guidance on OpenAI fine-tuning process.
"The course clearly lays out the workflow for submitting fine-tuning jobs with OpenAI."
"I found the steps for data formatting and uploading very easy to follow."
"The process shown for fine-tuning was well-explained and easy to replicate."
"Understanding the OpenAI specific JSONL format was crucial and well-covered."
Excellent hands-on experience with OpenAI tuning.
"The hands-on sessions with the OpenAI API were incredibly useful."
"Really enjoyed the practical workflow walkthrough for fine-tuning."
"This course gave me the practical steps needed to start fine-tuning on OpenAI."
"Focusing on the practical implementation using OpenAI was very helpful."
OpenAI API/libraries may change, affecting code.
"Had some issues with the code snippets due to recent OpenAI library updates."
"Be prepared that the OpenAI API can change, which might affect the code examples."
"The content is great but needs to keep pace with OpenAI's rapid changes to remain fully accurate."
"Staying current with the OpenAI API requires external effort."
Focuses only on OpenAI; minimal coverage of other methods.
"Could use more in-depth coverage on theoretical aspects or different fine-tuning techniques like LoRA."
"Wish it covered other LLMs or methods beyond OpenAI's specific API."
"Good for OpenAI, but don't expect a deep dive into general fine-tuning theory or other frameworks."
"The course is very specific to OpenAI, which is fine if that's all you need."

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 LLM Fine Tuning Fundamentals + Fine tune OpenAI GPT model with these activities:
Review LLM Fundamentals
Solidify your understanding of LLM fundamentals before diving into fine-tuning. This will provide a strong foundation for the course.
Browse courses on Large Language Models
Show steps
  • Review the basics of LLMs.
  • Understand the architecture of LLMs.
  • Familiarize yourself with common LLM applications.
Read 'Natural Language Processing with Transformers'
Deepen your understanding of Transformer models, which are the backbone of many LLMs. This book will provide a solid theoretical and practical foundation.
View Natural Language Processing with Transformers,... on Amazon
Show steps
  • Read the chapters on Transformer architecture.
  • Study the examples of using Transformers for NLP tasks.
  • Experiment with the code examples provided in the book.
Experiment with OpenAI Playground
Gain hands-on experience with the OpenAI Playground to understand how different parameters affect model output. This will help you fine-tune models more effectively.
Show steps
  • Explore different models available in the Playground.
  • Adjust parameters like temperature and top_p.
  • Analyze the impact of parameter changes on the generated text.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Document Fine-Tuning Experiments
Document your fine-tuning experiments, including the datasets used, parameters adjusted, and results obtained. This will help you track your progress and identify best practices.
Show steps
  • Choose a format for documenting your experiments.
  • Record the datasets used for fine-tuning.
  • Note the parameters adjusted during each experiment.
  • Analyze and document the results of each experiment.
Discuss Fine-Tuning Strategies
Collaborate with peers to discuss different fine-tuning strategies and share insights. This will broaden your understanding and expose you to new approaches.
Show steps
  • Organize a peer study group.
  • Share your fine-tuning experiments and results.
  • Discuss different fine-tuning strategies and their effectiveness.
Fine-Tune a GPT Model for a Specific Task
Apply your knowledge by fine-tuning a GPT model for a specific task, such as text summarization or question answering. This will solidify your skills and demonstrate your proficiency.
Show steps
  • Choose a specific task for fine-tuning.
  • Gather and prepare a dataset for the chosen task.
  • Fine-tune a GPT model using the prepared dataset.
  • Evaluate the performance of the fine-tuned model.
Build a Demo Application
Create a demo application that showcases the capabilities of your fine-tuned model. This will provide a tangible demonstration of your skills and knowledge.
Show steps
  • Design the user interface for the demo application.
  • Integrate the fine-tuned model into the application.
  • Test and refine the application's functionality.
  • Deploy the application for public access.
Read 'Generative AI with Python and TensorFlow 2'
Expand your knowledge of generative AI techniques beyond LLMs. This book will provide a broader context for understanding the field.
View Generative AI with Python and TensorFlow 2:... on Amazon
Show steps
  • Read the chapters on generative models.
  • Study the examples of implementing generative models in Python.
  • Experiment with the code examples provided in the book.

Career center

Learners who complete LLM Fine Tuning Fundamentals + Fine tune OpenAI GPT model will develop knowledge and skills that may be useful to these careers:
Large Language Model Engineer
A Large Language Model Engineer focuses on the development, deployment, and maintenance of LLMs. This course directly aligns with the practical skills required, particularly in fine tuning models. The course covers the fundamentals of fine tuning, its importance, and the workflow involved, all crucial aspects for an Large Language Model Engineer. Moreover, the hands-on sessions on fine tuning OpenAI's GPT model, including data preparation, formatting, and evaluation, provide invaluable experience. This specialized knowledge can assist an engineer in adapting LLMs to specific organizational needs. The hands-on sessions covering the entire fine-tuning workflow to fine tune OpenAI GPT model will be very useful.
See salaries and explore the career path for Large Language Model Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer specializes in developing systems that can understand and process human language, often relying on LLMs. This course perfectly aligns with enhancing the skills required for this role. The course covers the fundamentals of fine tuning LLMs, a crucial skill for tailoring these models to specific language-related tasks. The hands-on experience with OpenAI's GPT model assists a Natural Language Processing Engineer in formatting data, executing fine tuning processes, and evaluating the results, thereby ensuring models are optimized for their intended use. The sections on the OpenAI specialized JSONL format and the roles of system, user, and assistant are particularly valuable.
See salaries and explore the career path for Natural Language Processing Engineer
Computational Linguist
A Computational Linguist applies computational techniques to analyze and process human language, often working with LLMs to improve language understanding and generation. This course aligns well with the needs of this role. The course covers the fundamentals of fine tuning LLMs, which is crucial for tailoring these models to specific linguistic tasks. The hands-on experience with OpenAI's GPT model assists a Computational Linguist in formatting data and evaluating the results, thereby ensuring models are optimized for their intended use. The instructor's discussion of the roles of system, user, and assistant are highly relevant.
See salaries and explore the career path for Computational Linguist
Generative AI Specialist
A Generative AI Specialist focuses on creating models that generate new content, such as text or images, often employing LLMs. This course directly supports the skills required to fine tune these models effectively. The course's coverage on LLM basics, the importance of fine tuning, and practical sessions on OpenAI's GPT model are invaluable. By learning to prepare datasets, execute fine tuning processes, and evaluate model outcomes, a Generative AI Specialist can ensure that the models meet specific creative requirements. The detailed walkthrough of the OpenAI dashboard and playground is also highly relevant. Such a specialist will benefit from the bonus lecture.
See salaries and explore the career path for Generative AI Specialist
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models, including LLMs. The course can prove valuable. The course covers the fundamentals of LLM fine tuning, which is a critical skill for Machine Learning Engineers who need to adapt pre-trained models for specific tasks. The practical sessions dedicated to fine tuning OpenAI's GPT model, including data formatting and evaluation, provide hands-on experience that is directly applicable to real-world scenarios. The course can help build a strong understanding of how to tailor LLMs to meet unique requirements. The sections on creating and submitting a fine tuning job to OpenAI will be particularly relevant.
See salaries and explore the career path for Machine Learning Engineer
Machine Learning Operations Engineer
A Machine Learning Operations Engineer focuses on deploying and maintaining machine learning models in production environments, including LLMs. This course provides relevant knowledge for optimizing LLM performance. The course covers the practical aspects of fine tuning, such as preparing data, creating fine tuning jobs, and evaluating model outcomes. By understanding the fine tuning workflow, a Machine Learning Operations Engineer can streamline the deployment and monitoring of LLMs, ensuring they meet specific performance criteria. The section on calculating the token count may be especially useful.
See salaries and explore the career path for Machine Learning Operations Engineer
AI Consultant
An AI Consultant advises organizations on how to implement AI solutions, including those based on LLMs. This course provides a practical understanding of LLM fine tuning, which is essential for providing informed recommendations. The course covers the fundamentals of fine tuning, the workflow involved, and hands-on experience with OpenAI's GPT model. By learning how to fine tune LLMs, an AI Consultant can better assess the feasibility and impact of AI projects, ensuring that solutions are tailored to specific client needs. The section on fine tuning best practices is particularly relevant.
See salaries and explore the career path for AI Consultant
AI Product Manager
An AI Product Manager oversees the development and launch of AI-powered products, including those leveraging LLMs. This course provides a valuable understanding of the capabilities and limitations of fine tuning LLMs. The course covers the fundamentals, the fine tuning workflow, and practical sessions on OpenAI's GPT model, which are crucial for making informed product decisions. By understanding the effort and resources required to fine tune LLMs, an AI Product Manager can better estimate project timelines and allocate resources effectively. The course module discussing when fine tuning is needed will be most relevant.
See salaries and explore the career path for AI Product Manager
Solutions Architect
A Solutions Architect designs and implements technology solutions, often integrating AI and LLMs to solve business problems. This course can be useful by providing a practical understanding of how LLMs can be customized through fine tuning. The course covers the process of preparing data, executing fine tuning jobs, and evaluating model performance, all essential for designing effective AI solutions. Hands-on experience with OpenAI's GPT model can inform decisions about which models to use and how to adapt them. The section on fine tuning best practices should be particularly useful.
See salaries and explore the career path for Solutions Architect
AI Research Scientist
An AI Research Scientist conducts research to advance the field of artificial intelligence, which includes exploring and improving LLMs. This course can be useful by providing practical knowledge of LLM fine tuning. The course covers fundamentals, different types of fine tuning, and best practices, all critical for informed research. Moreover, hands-on experience with OpenAI's GPT model assists an AI Research Scientist in understanding the nuances of fine tuning, data formatting, and outcome evaluation. This can help an AI Research Scientist to investigate new methods for improving LLM performance. Those with an interest in RLHF finetuning may find this course especially useful.
See salaries and explore the career path for AI Research Scientist
Data Scientist
A Data Scientist analyzes data and builds models to solve business problems, and increasingly, this involves LLMs. This course may be useful by providing a foundational understanding of LLM fine tuning. By learning how to prepare datasets, execute fine tuning processes, and evaluate model outcomes, a Data Scientist can enhance their ability to leverage LLMs effectively. The course’s emphasis on practical sessions and step-by-step guidance in fine tuning OpenAI's GPT model on a custom dataset is particularly valuable. Taking this course can help Data Scientists tailor LLMs to specific analytical tasks. The instructor's review of Exploratory Data Analysis may be especially pertinent.
See salaries and explore the career path for Data Scientist
Artificial Intelligence Developer
An Artificial Intelligence Developer builds and implements AI solutions, often involving LLMs. This course may be helpful by providing a solid understanding of LLM fine tuning techniques. As the course provides hands-on experience in fine tuning OpenAI's GPT model, an Artificial Intelligence Developer can leverage this knowledge to customize models for specific applications. The modules on data preparation, Exploratory Data Analysis, and model evaluation are particularly relevant. This course may assist someone in ensuring that the models are well-suited to their intended tasks. The sections on OpenAI dashboard and playground will also be relevant to Artificial Intelligence Developers.
See salaries and explore the career path for Artificial Intelligence Developer
Prompt Engineer
A Prompt Engineer crafts effective prompts to elicit desired responses from Large Language Models. This course may be useful by demonstrating how the responses of LLMs may be modified. The course provides hands-on experience in fine tuning OpenAI's GPT model. Learning more about the model itself and what it outputs, a Prompt Engineer can better optimize the prompts that are consumed by it. The sections on the OpenAI specialized JSONL format and the roles of system, user, and assistant may be particularly valuable.
See salaries and explore the career path for Prompt Engineer
Data Architect
A Data Architect designs and manages data systems, including those that support LLMs. This course may be useful by providing insights into the data requirements and processes involved in fine tuning LLMs. The course's focus on preparing and formatting datasets, as well as understanding the OpenAI specialized JSONL format, is highly relevant for a Data Architect. By understanding the end-to-end fine tuning workflow, a Data Architect can better design data pipelines and storage solutions that support AI initiatives. The discussion of token count calculation may also be applicable.
See salaries and explore the career path for Data Architect
Data Engineer
A Data Engineer builds and maintains the infrastructure required for data storage and processing, including supporting LLM-based applications. This course may be useful by providing insights into the data preparation steps involved in fine tuning LLMs. The course covers data preprocessing, data formatting, and converting data into the OpenAI specialized JSONL format, all relevant for designing efficient data pipelines. By understanding these requirements, a Data Engineer can ensure that the data infrastructure supports the needs of AI initiatives. The section on data preprocessing is especially relevant.
See salaries and explore the career path for Data Engineer

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 LLM Fine Tuning Fundamentals + Fine tune OpenAI GPT model.
Cover image
Natural Language Processing with Transformers,...
Save
Provides a comprehensive guide to using Transformers for NLP tasks. It covers the theory behind Transformers and provides practical examples of how to use them. It valuable resource for anyone who wants to learn more about Transformers and how to use them for NLP. This book provides additional depth to the course.
Natural Language Processing with Transformers,...
Paperback
$$
Natural Language Processing with Transformers,...
Kindle Edition
$$
Cover image
Generative AI with Python and TensorFlow 2
Save
Explores generative AI techniques using Python and TensorFlow 2. It covers various generative models, including GANs and VAEs, and provides practical examples of how to implement them. While not directly focused on LLMs, it provides a broader context for understanding generative models and their applications. This book is more valuable as additional reading than as a current reference.
Generative AI with Python and TensorFlow 2: Create...
Paperback
$$
Generative AI with Python and TensorFlow 2: Create...
Kindle Edition
$$

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Rating
4.6 Filled star Filled star Filled star Filled star Half star
Effort
3.5 total hours
Via
Udemy
Instructor
J Garg - Real Time Learning
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Provides hands-on experience fine-tuning OpenAI's GPT model, which is a highly sought-after skill in the field of applied artificial intelligence
Covers the OpenAI specialized JSONL format, which is essential for preparing data for fine-tuning and understanding the roles of system, user, and assistant
Explores various LLM fine-tuning techniques, including standard, sequential, layer-wise, feature extraction, RLHF, PEFT, LoRA, QLoRA, and instruction fine-tuning, offering a comprehensive overview
Requires familiarity with Python and basic data manipulation techniques, which may pose a barrier for learners without a programming background
Focuses specifically on fine-tuning OpenAI's GPT model, which may limit its applicability to other LLMs or platforms, but allows for deep expertise
Teaches how to calculate token count using the Tiktoken library, which is crucial for estimating fine-tuning costs in advance and managing resource allocation
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