Finetuning Large Language Models
Instructors: Sharon Zhou
- Intermediate
- 1 Hour 25 Minutes
- 9 Video Lessons
- 6 Code Examples
What you'll learn
Learn the fundamentals of finetuning a large language model (LLM).
Understand how finetuning differs from prompt engineering, and when to use both.
Get practical experience with real data sets, and how to use techniques for your own projects.
About this course
Join our new short course, Finetuning Large Language Models! Learn from Sharon Zhou, Co-Founder and CEO of Lamini, and instructor for the GANs Specialization and How Diffusion Models Work.
When you complete this course, you will be able to:
- Understand when to apply finetuning on LLMs
- Prepare your data for finetuning
- Train and evaluate an LLM on your data
With finetuning, you’re able to take your own data to train the model on it, and update the weights of the neural nets in the LLM, changing the model compared to other methods like prompt engineering and Retrieval Augmented Generation. Finetuning allows the model to learn style, form, and can update the model with new knowledge to improve results.
Who should join?
Learners who want to understand the techniques and applications of finetuning, with Python familiarity, and an understanding of a deep learning framework such as PyTorch.
Course Outline
9 Lessons・6 Code ExamplesIntroduction
Video・2 mins
Why finetune
Video with code examples・14 mins
Where finetuning fits in
Video with code examples・15 mins
Instruction finetuning
Video with code examples・9 mins
Data preparation
Video with code examples・10 mins
Training process
Video with code examples・16 mins
Evaluation and iteration
Video with code examples・11 mins
Consideration on getting started now
Video・4 mins
Conclusion
Video・1 min
Instructor
Sharon Zhou
Co-Founder and CEO of Lamini
Finetuning Large Language Models
- Intermediate
- 1 Hour 25 Minutes
- 9 Video Lessons
- 6 Code Examples
Course access is free for a limited time during the DeepLearning.AI learning platform beta!
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