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Large language Models for Text Generation

Course for anyone who is fascinated by the capabilities of large language models (LLMs) and generative artificial intelligence, and wants to delve deeper into the subject.
Level
Designed for participants with basic knowledge and experience
intermediate
Course length
1 day
Language
 cz  eu
Course code
PU23110153
Artificial intelligence (AI)
Category:
Do you want this tailor-made course to your company? Contact us

Courses with lecturer

Term
Language
Place
Form
?
How and where the course takes place.
Price without VAT
Open term
?
We will agree on a specific date together. This is a non-binding order.
Language
Place
Praha
Form
classroom
?
The course with an instructor in classroom.
Code of the course: PU23110153-0001
Price without VAT
4 990 Kč
Open term
?
We will agree on a specific date together. This is a non-binding order.
Language
Place
online
Form
virtual classroom
?
Online training with a lecturer at a specific time.
Code of the course: PU23110153-0002
Price without VAT
4 990 Kč

Course description

We will learn about transformers, the basic building blocks of modern language models, introduce the most well-known architectures, and show how large language models can be used for a variety of applications. No paid third-party account is required for the hands-on exercises. We’ll use open-source models that, when used properly, are just as good as the biggest commercial models.

Required knowledge

  • Basic knowledge of programing in Python
  • Basics of machine learning on the level of our course Introduction to  machine Learning

Target audience

This course is designed for anyone who is fascinated by the capabilities of large language models (LLMs) and generative artificial intelligence, and wants to delve into the subject beyond the basic user level.

Course content

  • Overview of generative AI (text, images)
  • Evolution of language modeling
  • Transformers
  • Types of transformer-based LLMs (encoder, decoder, encoder-decoder)
  • Reinforcement learning with human feedback
  • Overview of the most popular transformer-based LLMs (BERT, GPT, LLAMA, T5, BART…)
  • Transformer-based classification example with HuggingFace in Google Colab
  • Prompt engineering: in-context learning, zero shot, one shot and few shot prompting, configuration parameters of the generative process
  • Practical examples of in-context learning with HuggingFace in Google Colab
  • Full fine-tuning of large language models, parameter-efficient fine-tuning (LoRA)
  • Text generative AI evaluation (ROUGE, BLEU)
  • Practical example of parameter efficient fine-tuning with HuggingFace in Google Colab
  • Retrieval Augmented Generation (RAG)

Do you want this tailor-made course for your company?

Contact us

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Do you want this tailor-made course for your company?

Contact us

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Artificial intelligence (AI) 15. 11. 2024
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