e-learning
Pretraining a Large Language Model (LLM) from Scratch on DNA Sequences
Abstract
Generative Artificial Intelligence (AI) represents a cutting-edge domain within machine learning, focused on creating new, synthetic yet realistic data. This includes generating text, images, music, and even biological sequences. At the heart of many generative AI applications are Large Language Models (LLMs), which have revolutionized natural language processing and beyond.
About This Material
This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.
Questions this will address
- How to load and configure a pre-trained language model for DNA sequence analysis?
- What is the process for tokenizing DNA sequences to prepare them for model training?
- How to split and organize DNA sequence dataset for effective model training and evaluation?
- What are the key hyperparameters to consider when pretraining a language model on DNA sequences, and how to configure them?
- How to use a trained language model to generate and interpret embeddings for DNA sequences?
Learning Objectives
- Identify and load a pre-trained language model (LLM) suitable for DNA sequence analysis.
- Explain the role of a tokenizer in converting DNA sequences into numerical tokens for model processing.
- Prepare and tokenize DNA sequence datasets for model training and evaluation.
- Configure and implement data collation to organize tokenized data into batches for efficient training.
- Define and configure hyperparameters for pretraining a model, such as learning rate and batch size.
- Monitor and evaluate the model's performance during training to ensure effective learning.
- Use the trained model to generate embeddings for DNA sequences and interpret these embeddings for downstream bioinformatics applications.
- Develop a complete workflow for training a language model on DNA sequences, from data preparation to model evaluation, and apply it to real-world bioinformatics tasks.
Licence: Creative Commons Attribution 4.0 International
Keywords: AI & ML, ELIXIR, Large Language Model, Statistics and machine learning, jupyter-notebook, work-in-progress
Target audience: Students
Resource type: e-learning
Version: 2
Status: Draft
Prerequisites:
- Deep Learning (without Generative Artificial Intelligence) using Python
- Foundational Aspects of Machine Learning using Python
- Introduction to Python
- Neural networks using Python
- Python - Warm-up for statistics and machine learning
Learning objectives:
- Identify and load a pre-trained language model (LLM) suitable for DNA sequence analysis.
- Explain the role of a tokenizer in converting DNA sequences into numerical tokens for model processing.
- Prepare and tokenize DNA sequence datasets for model training and evaluation.
- Configure and implement data collation to organize tokenized data into batches for efficient training.
- Define and configure hyperparameters for pretraining a model, such as learning rate and batch size.
- Monitor and evaluate the model's performance during training to ensure effective learning.
- Use the trained model to generate embeddings for DNA sequences and interpret these embeddings for downstream bioinformatics applications.
- Develop a complete workflow for training a language model on DNA sequences, from data preparation to model evaluation, and apply it to real-world bioinformatics tasks.
Date modified: 2025-04-25
Date published: 2025-04-17
Contributors: Anup Kumar, Björn Grüning, Bérénice Batut, Wandrille Duchemin, olisand
Scientific topics: Statistics and probability
Activity log
