Llm - Fine Tune With Custom Data

What you'll 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
Requirements
Basic python knowledge
Description
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 TuningIn 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 ModelsThis 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 tuneGradient 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 modelLearn 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!
Overview
Section 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
Section 2: Fine tune using GPT models
Lecture 7 Models availability, and use cases
Lecture 8 Prepare the sample data
Lecture 9 Format the sample data
Lecture 10 Token counting function
Lecture 11 Check warning and OpenAI cost
Lecture 12 Understanding model fine-tuning
Lecture 13 Training vs Validation data
Lecture 14 Uploading training and validation data to OpenAI
Lecture 15 Create a fine tune job
Lecture 16 QA using your new model
Section 3: Fine tune using gradient platform
Lecture 17 Gradient platform - Setting up login
Lecture 18 Gradient platform - Interface
Lecture 19 What are some of the pre-trained model available?
Lecture 20 Create a new model with sample data
Lecture 21 What is epochs?
Lecture 22 Fine tuning the model and QA
Section 4: Elon Musk tweet generator
Lecture 23 Prepare the datasets with OpenAI
Lecture 24 Create a fine-tune model
Lecture 25 Testing the model in OpenAI playground
Lecture 26 Elon Musk Tweet Generator Streamlit app
Section 5: Data Extraction fine-tune model
Lecture 27 Extract any valuable information from raw text
Section 6: Congratulations and Thank You!
Lecture 28 Your feedback is very valuable!
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.
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