LLM - Fine tune with custom data from Udemy

Welcome to LLM - Fine Tune with Custom Data.

If you're passionate about taking your machine learning skills to the next level, this course is tailor-made for you. Get ready to embark on a learning journey that will empower you to fine-tune language models with custom datasets, unlocking a realm of possibilities for innovation and creativity.

Introduction to LLM and Fine Tuning

In this opening section, you'll be introduced to the course structure and objectives. We'll explore the significance of fine-tuning in enhancing language models and delve into the foundational models that set the stage for customization. Discover the reasons behind the need for fine-tuning and explore various strategies, including an understanding of critical model parameters. Gain a comprehensive understanding of the fundamental principles and advanced concepts in artificial intelligence and language modeling.

Fine Tune Using GPT Models

This section focuses on practical applications. Survey available models and their use cases, followed by essential steps in preparing and formatting sample data. Understand token counting and navigate potential pitfalls like warnings and cost management. Gain a comprehensive understanding of the fine-tuning process, differentiating between training and validation data. Learn to upload data to OpenAI, create a fine-tune job, and ensure quality assurance for your model.

Use Gradient Platform to quickly fine tune

Gradient AI Platform : The only AI Agent platform that supports fine-tuning, RAG development, and purpose built LLMs out-of-the-box. Pre-tuned, Domain Expert AI i.e. Gradient offers domain-specific AI designed for your industry. From healthcare to financial services, we've built AI from the ground up to understand domain context. Use the platform to upload and train base foundations models with your own dataset.

Create a Elon Musk Tweet Generator

Train a foundation model with Elon Mush sample tweets, and then used the 'New Fine Tune Model' to create Elon Mush style tweets. Create a streamlit app to demonstrate side-by-side a normal tweet generated by OpenAI vs your very own model.

Data Extraction fine-tune model

Learn how to extract 'valuable information' from a raw text. Learn how to pass sample datasets with question and answers, and then pass any raw text to get valuable information. Use real-world example of identifying person, amount spend and item from raw expense transactions and much more.

Enroll now to learn how to fine-tune large language models with your own data, and unlock the potential of personalized applications and innovations in the world of machine learning.

What's inside

Syllabus

Introduction

What is fine-tuning?

Training vs Fine-tuning

The Foundation models

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores fine-tuning strategies, including understanding critical model parameters, which is essential for customizing language models and enhancing their performance for specific tasks

Uses GPT models and the Gradient AI Platform, which allows learners to gain practical experience in fine-tuning with industry-relevant tools and platforms

Covers token counting and cost management, which are crucial for efficiently utilizing resources when working with large language models and avoiding unexpected expenses

Includes a section on data extraction fine-tuning, which teaches how to extract valuable information from raw text using question-and-answer datasets, a practical skill for real-world applications

Requires learners to upload data to OpenAI, which may involve costs depending on the volume of data and usage, potentially creating a barrier for some learners

Includes quantization techniques, such as symmetric and asymmetric quantization, which are essential for optimizing LLMs for deployment on resource-constrained devices

Reviews summary

Practical llm fine-tuning with projects

According to learners, this course offers a strong foundation (positive) in fine-tuning Large Language Models, particularly highlighting its practical, hands-on approach (positive). Students frequently praised the useful projects (positive), such as building an Elon Musk tweet generator and a data extraction model, which provide valuable real-world application. The curriculum is noted for covering multiple popular frameworks and platforms (positive), including OpenAI's API, Gradient, Unsloth, and LLaMA Factory. While the course is seen as highly relevant, some reviewers indicated that it assumes a certain level of prior machine learning knowledge (warning), which could be challenging for beginners. Additionally, while comprehensive, certain sections, notably on quantization and underlying math (neutral), were felt by some to be presented too quickly (warning). Overall, it is viewed as a valuable resource for applying fine-tuning techniques.

Some topics could use more detail.

"The math and quantization sections felt a bit rushed; I had to seek external resources."

"While broad, deeper dives into specific optimization techniques would enhance the course."

"Overall pace was good, but the transition between theory and advanced topics was sometimes abrupt."

"Could use more explanation on selecting hyperparameters for fine-tuning."

Explores diverse tools and frameworks.

"Appreciated that the course covered more than just OpenAI's API for fine-tuning."

"The sections on Unsloth and LLaMA Factory introduced me to powerful alternative approaches."

"Learning how to utilize platforms like Gradient was a key benefit of this course."

"Comparing different fine-tuning methods gave me a broader perspective."

Valuable hands-on application examples.

"The practical examples, especially the Tweet generator project, were incredibly helpful for understanding."

"I found the data extraction project immediately applicable to tasks I face at work."

"The hands-on labs integrating code and theory made the concepts stick."

"Creating the Streamlit app helped consolidate everything learned."

Requires foundational ML background.

"Definitely need a solid understanding of machine learning and Python before taking this."

"Some sections felt like they moved too fast if you weren't already familiar with the concepts."

"Could be challenging for absolute beginners in the ML/LLM space."

"Make sure your prerequisites are strong before diving in."

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 tune with custom data with these activities:

Review Foundation Models

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Solidify your understanding of foundation models to better grasp the fine-tuning process.

Browse courses on Foundation Models

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Read articles about different foundation models.
Compare the architectures of different models.
Summarize the strengths and weaknesses of each model.

Read 'Natural Language Processing with Transformers'

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Gain a deeper understanding of the Transformer architecture used in many LLMs.

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

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Read the chapters on Transformer architecture.
Experiment with the code examples in the book.
Relate the concepts to the course material.

Practice Token Counting

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Improve your ability to accurately count tokens, which is crucial for cost management and data preparation.

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Write a script to count tokens in a text file.
Test the script with different text samples.
Compare your results with online token counters.

Four other activities

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Read 'Hugging Face Transformers'

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Learn how to effectively use the Hugging Face Transformers library for fine-tuning.

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

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Work through the code examples in the book.
Adapt the examples to your own projects.
Explore the different features of the library.

Document Your Fine-Tuning Process

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Reinforce your understanding by documenting the steps you took to fine-tune a model.

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Choose a fine-tuning project.
Document each step of the process.
Include code snippets and explanations.
Share your documentation with others.

Build a Q&A System

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Apply your fine-tuning skills to create a practical question-answering system using custom data.

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Gather a dataset of questions and answers.
Fine-tune a language model on the dataset.
Build an interface for users to ask questions.
Evaluate the performance of the system.

Contribute to an Open Source LLM Project

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Deepen your understanding by contributing to a real-world LLM project.

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Find an open-source LLM project on GitHub.
Identify an area where you can contribute.
Submit a pull request with your changes.
Respond to feedback from the project maintainers.

Career center

Learners who complete LLM - Fine tune with custom data will develop knowledge and skills that may be useful to these careers:

Natural Language Processing Engineer

A Natural Language Processing Engineer specializes in developing algorithms that enable computers to understand and process human language. This course, with its focus on fine-tuning language models, directly supports the core skills needed for this profession. You will be able to customize models for specific language-related tasks, improving their performance and accuracy. The practical experience gained in preparing data, training models, and ensuring quality assurance will improve your capabilities as an NLP Engineer. The segment on LLaMA Factory will also be of particular benefit.

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

Machine Learning Engineer

A Machine Learning Engineer develops, tests, and deploys machine learning models. This course, centered on fine-tuning with custom data, directly aligns with the practical skills required for this role. The knowledge gained from this course will enhance your ability to customize and optimize language models for specific tasks, a crucial aspect of a Machine Learning Engineer's responsibilities. Understanding model parameters, data preparation, and quality assurance will help you excel in this career. You may find the segments on GPT models and data extraction models particularly useful as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Artificial Intelligence Developer

An Artificial Intelligence Developer designs and implements AI solutions using various machine learning techniques. This course provides hands-on experience in fine-tuning language models with custom datasets, which is highly relevant to this role. AI Developers often need to customize models for specific applications; this course provides practical knowledge in data preparation, model training, and quality assurance. The course can help you master the customization process that is essential to your success. The sections on pre-trained models, Quantization and Unsloth will be of particular interest.

See salaries and explore the career path for Artificial Intelligence Developer

Data Scientist

A Data Scientist analyzes large datasets to extract meaningful insights and build predictive models. The ability to fine-tune language models, as taught in this course, is valuable for enhancing the accuracy and relevance of these models. Data Scientists can leverage fine-tuning to customize models for specific domains or tasks, leading to more effective analysis and predictions. In your training as a Data Scientist, you may find the sections on data extraction fine-tune models and preparing sample data particularly applicable.

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Computational Linguist

A Computational Linguist combines linguistics and computer science to develop language-based technologies. This course, with its focus on fine-tuning language models, provides practical skills in customizing models for specific linguistic tasks. Computational Linguists might use such language models in translation, summarization, or speech recognition. The sections on data extraction and fine tuning with LLaMA factory will be of immediate value.

See salaries and explore the career path for Computational Linguist

Machine Learning Researcher

A Machine Learning Researcher investigates and develops new algorithms and techniques in machine learning. The course provides a practical understanding of fine-tuning language models that Machine Learning Researchers can leverage to explore novel approaches to model customization and optimization. Understanding the nuances of fine-tuning, model parameters, and data preparation can support your innovative work. The sections on the math behind fine-tuning and quantization will be immediately valuable to you.

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AI Consultant

As an AI Consultant, your job involves advising organizations on how to implement AI solutions to improve their business processes. This course can provide you with hands-on experience in fine-tuning language models, allowing you to offer practical guidance on customizing models for specific client needs. Understanding model parameters, data preparation, and quality assurance will enhance your consulting capabilities. You will be armed to propose real-world solutions. The sections on fine-tuning use cases and model availability will likely be immediately useful to you.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications. This course can provide you with practical skills in integrating fine-tuned language models into software products. Understanding the fine-tuning process, data preparation, and model parameters can help you build AI-powered features. The sections on Gradient AI platforms and Streamlit apps will be useful in product development.

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AI Product Manager

An AI Product Manager oversees the development and launch of AI-powered products. While this role is less technical, understanding the capabilities and limitations of fine-tuning language models, as covered in this course, can inform product strategy and feature prioritization. The knowledge gained will help you make informed decisions about model customization and optimization. You will have a better appreciation for the range of possibilities. The segments on training versus fine-tuning and OpenAI cost may be particularly useful to you.

See salaries and explore the career path for AI Product Manager

Business Intelligence Analyst

A Business Intelligence Analyst analyzes business data to identify trends and insights that support decision-making. The skills gained from this course in preparing and formatting data for fine-tuning can support your ability to work with machine learning teams. Understanding the data requirements for model customization helps to improve data analysis workflows. The sections on preparing the sample data and format the sample data are directly applicable.

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Data Analyst

A Data Analyst gathers, cleans, and analyzes data to identify trends and insights. While not directly focused on model building, the skills gained from this course in preparing and formatting data for fine-tuning can enhance your ability to work with machine learning teams. Understanding the data requirements for model customization can improve your data analysis workflows. It may also enable you to contribute to machine learning projects. The segments on preparing the sample data and format the sample data sections could be directly applicable to your work.

See salaries and explore the career path for Data Analyst

Data Engineer

A Data Engineer builds and maintains the infrastructure required for data storage and processing. While not directly focused on model building, this course can provide you with valuable insights into the data requirements for fine-tuning language models. Understanding the data preparation steps can help you optimize data pipelines and storage solutions. The sections on uploading training and validation data will be particularly insightful.

See salaries and explore the career path for Data Engineer

AI Ethicist

An AI Ethicist focuses on the ethical implications of artificial intelligence, including bias and fairness in machine learning models. While this course is technical, understanding the fine-tuning process and the impact of custom data can inform ethical evaluations of AI systems. The knowledge gained can help you assess potential biases introduced during model customization. Consider how this course prepares you to assess fairness in downstream applications.

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Technical Writer

A Technical Writer creates documentation for software and hardware products. This course can provide you with a deeper understanding of the concepts and processes behind fine-tuning language models, enabling you to create more informative and accurate documentation for AI tools and technologies. You will be able to explain complex concepts in a meaningful way. The sections on model parameters and ways to fine-tune a model may be relevant to you.

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Robotics Engineer

A Robotics Engineer designs, builds, and programs robots. This course, while seemingly unrelated, may provide skills in customizing language models for natural language interfaces. Understanding the fine-tuning process can enable you to create more intuitive and responsive robotic systems. The sections on creating a new model with sample data may be particularly informative.

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LLM - Fine tune with custom data

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Practical llm fine-tuning with projects

Activities

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