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Fine Tuning LLMs
Customize open source models and create yourGPT.

About

Before starting the course, let's go over the content and what you need before jumping in.

 

This course covers the latest strategies for fine tuning LLMs, building your own models, and working with open source weights.

Is this course for you?

Before jumping into this course, I would recommend understanding ChatGPT at a high level and having a working knowledge of Python.

 

Many teams would like to have their own GPT that is trained on company data. If you’re looking to break into data science or ML, understanding how to fine tune LLMs will be a huge boost. If you want to build side projects or make GPT perform better for your use case, this course is also for you.

This course is divided into 3 parts and over 10 total modules:

 

Topics

  1. LLMs Background Review​

  2. HuggingFace's Transformers and Tokenizers Libraries

  3. Fine Tuning GPT2 (back when OpenAI was open...)

  4. LoRA (Low Rank Adaptation) as a form of parameter efficient fine tuning

  5. Quantization and the BitsAndBytes library

  6. Fine Tuning LLaMA 2 (Meta's latest LLM)

  7. BERT and alternatives to GPT

Check out the previews below, and we can get started customizing LLMs. If you ever have any questions while watching the modules, you can shoot me an email as well!

Large Language Models (LLMs) Review
Machine Learning Datasets Review
Training Large Language Models (LLMs) Review

Testimonials

See what experts have to say.

GPT & Chill Testimonial

- Rafael, a Software Engineer

GPT & Chill Testimonial

- Jane, a PhD student

GPT & Chill Testimonial

- Simon, a data science lead

Fine Tuning LLMs
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LLMs Background Review

LLMs Background Review

$
22:02
Introducing New Libraries

Introducing New Libraries

$
05:34
Fine Tuning GPT-2: Dataset, Model, Tokenizer, and Config

Fine Tuning GPT-2: Dataset, Model, Tokenizer, and Config

$
18:58
Fine Tuning GPT-2: Generation, More Training, and Inference

Fine Tuning GPT-2: Generation, More Training, and Inference

$
16:46

Note for Members: The Video Info tab has a description with important clarifications and information for each module.

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