LLM - Fine tune with custom data

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!