LLM – Fine tune with custom data

LLM – Fine tune with custom data, available at $54.99, has an average rating of 4.53, with 51 lectures, based on 94 reviews, and has 999 subscribers.

You will learn about Understanding Fine tuning vs training data Fine tune using GPT models, GPT 3.5 Turbo models, Open AI models Preparing, creating, and uploading training and validation datasets Fine tuning using Gradient Platform Create Elon Mush Tweet Generator Build a data extraction fine-tune model This course is ideal for individuals who are Anyone who want to explore the world of AI or Anyone who want to step into AI world with practical fine tuning models or Data engineers, database administrators and data professionals curious about the emerging field of model fine tuning or Software developers interested in integrating their own data into large language models or Data scientists and machine learning engineers. It is particularly useful for Anyone who want to explore the world of AI or Anyone who want to step into AI world with practical fine tuning models or Data engineers, database administrators and data professionals curious about the emerging field of model fine tuning or Software developers interested in integrating their own data into large language models or Data scientists and machine learning engineers.

Enroll now: LLM – Fine tune with custom data

Summary

Title: LLM – Fine tune with custom data

Price: $54.99

Average Rating: 4.53

Number of Lectures: 51

Number of Published Lectures: 48

Number of Curriculum Items: 51

Number of Published Curriculum Objects: 48

Original Price: $74.99

Quality Status: approved

Status: Live

What You Will Learn

Understanding Fine tuning vs training data
Fine tune using GPT models, GPT 3.5 Turbo models, Open AI models
Preparing, creating, and uploading training and validation datasets
Fine tuning using Gradient Platform
Create Elon Mush Tweet Generator
Build a data extraction fine-tune model

Who Should Attend

Anyone who want to explore the world of AI
Anyone who want to step into AI world with practical fine tuning models
Data engineers, database administrators and data professionals curious about the emerging field of model fine tuning
Software developers interested in integrating their own data into large language models
Data scientists and machine learning engineers.

Target Audiences

Anyone who want to explore the world of AI
Anyone who want to step into AI world with practical fine tuning models
Data engineers, database administrators and data professionals curious about the emerging field of model fine tuning
Software developers interested in integrating their own data into large language models
Data scientists and machine learning engineers.

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is fine-tuning?

Lecture 2: Training vs Fine-tuning

Lecture 3: The Foundation models

Lecture 4: Why Fine-tune?

Lecture 5: Ways to fine-tune a model

Lecture 6: Model parameters

Chapter 2: Fine tune using GPT models

Lecture 1: Models availability, and use cases

Lecture 2: Prepare the sample data

Lecture 3: Format the sample data

Lecture 4: Token counting function

Lecture 5: Check warning and OpenAI cost

Lecture 6: Understanding model fine-tuning

Lecture 7: Training vs Validation data

Lecture 8: Uploading training and validation data to OpenAI

Lecture 9: Create a fine tune job

Lecture 10: QA using your new model

Chapter 3: Fine tune using gradient platform

Lecture 1: Gradient platform – Setting up login

Lecture 2: Gradient platform – Interface

Lecture 3: What are some of the pre-trained model available?

Lecture 4: Create a new model with sample data

Lecture 5: What is epochs?

Lecture 6: Fine tuning the model and QA

Chapter 4: Elon Musk tweet generator

Lecture 1: Prepare the datasets with OpenAI

Lecture 2: Create a fine-tune model

Lecture 3: Testing the model in OpenAI playground

Lecture 4: Elon Musk Tweet Generator Streamlit app

Chapter 5: Data Extraction fine-tune model

Lecture 1: Extract any valuable information from raw text

Chapter 6: The Math behind Fine Tuning

Lecture 1: Quantization

Lecture 2: Custom precision and inference

Lecture 3: Floating point to binary representation

Lecture 4: Symmetric quantization

Lecture 5: Asymmetric quantization

Lecture 6: Post training quantization

Lecture 7: Quantization aware training (QAT)

Chapter 7: Quantizing any LLM

Lecture 1: Base model to GGUF model

Lecture 2: Quantization, and uploading to Huggingface

Chapter 8: Fine Tuning with Unsloth

Lecture 1: Introduction to Unsloth Framework

Lecture 2: Install unsloth FastLanguageModel

Lecture 3: Load IMDB Movie datasets

Lecture 4: Setup Model, Tokenizer and get PEFT Model

Lecture 5: Setup supervise fine tune trainer (SFTTrainer) model

Lecture 6: Test and save the model

Chapter 9: Fine Tune any LLMs using LLaMA Factory

Lecture 1: Create a fine tune model for docker commands NLP

Lecture 2: Train the model with LLaMA Factory

Chapter 10: Fine-Tuning Use Cases

Lecture 1: Fine-tuning LLMs are cheaper and faster

Lecture 2: Use Cases of Fine Tune Models

Lecture 3: When not to use Fine Tuning

Chapter 11: Congratulations and Thank You!

Lecture 1: Your feedback is very valuable!

Instructors

Adnan Waheed
Founder KlickAnalytics and ex-Bloomberg employee

Rating Distribution

1 stars: 0 votes
2 stars: 3 votes
3 stars: 11 votes
4 stars: 22 votes
5 stars: 58 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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