IBM
Generative AI Engineering and Fine-Tuning Transformers
IBM

Generative AI Engineering and Fine-Tuning Transformers

This course is part of multiple programs.

Joseph Santarcangelo
Ashutosh Sagar
Fateme Akbari

Instructors: Joseph Santarcangelo

Sponsored by ARS SCINet/AI-COE

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

7 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

7 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Sought-after job-ready skills businesses need for working with transformer-based LLMs for generative AI engineering... in just 1 week.

  • How to perform parameter-efficient fine-tuning (PEFT) using LoRA and QLoRA

  • How to use pretrained transformers for language tasks and fine-tune them for specific tasks.

  • How to load models and their inferences and train models with Hugging Face.

Details to know

Shareable certificate

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Assessments

4 assignments

Taught in English
Recently updated!

September 2024

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There are 2 modules in this course

In this module, you will be introduced to Fine Tuning. You’ll get an overview of generative models and compare Hugging Face and PyTorch frameworks. You’ll also gain insights into model quantization and learn to use pre-trained transformers and then fine-tune them using Hugging Face and PyTorch.

What's included

5 videos4 readings2 assignments4 app items

In this module, you will gain knowledge about parameter efficient fine-tuning (PEFT) and also learn about adapters such as LoRA (Low-Rank Adaptation) and QLoRA (Quantized Low-Rank Adaptation). In hands-on labs you will train a base model and pre-train LLMs with Hugging Face.

What's included

4 videos4 readings2 assignments2 app items4 plugins

Instructors

Joseph Santarcangelo
IBM
33 Courses1,673,476 learners

Offered by

IBM

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