Efficient Fine-Tuning with Low-Rank Adaptation (LoRA) Training Course

Low-Rank Adaptation (LoRA) is a cutting-edge technique for efficiently fine-tuning large-scale models by reducing the computational and memory requirements of traditional methods. This course provides hands-on guidance on using LoRA to adapt pre-trained models for specific tasks, making it ideal for resource-constrained environments.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers and AI practitioners who wish to implement fine-tuning strategies for large models without the need for extensive computational resources.

By the end of this training, participants will be able to:

Understand the principles of Low-Rank Adaptation (LoRA).
Implement LoRA for efficient fine-tuning of large models.
Optimize fine-tuning for resource-constrained environments.
Evaluate and deploy LoRA-tuned models for practical applications.

Format of the Course

Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.

Course Customization Options

To request a customized training for this course, please contact us to arrange.

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Course Outline

Introduction to Low-Rank Adaptation (LoRA)

What is LoRA?
Benefits of LoRA for efficient fine-tuning
Comparison with traditional fine-tuning methods

Understanding Fine-Tuning Challenges

Limitations of traditional fine-tuning
Computational and memory constraints
Why LoRA is an effective alternative

Setting Up the Environment

Installing Python and required libraries
Setting up Hugging Face Transformers and PyTorch
Exploring LoRA-compatible models

Implementing LoRA

Overview of LoRA methodology
Adapting pre-trained models with LoRA
Fine-tuning for specific tasks (e.g., text classification, summarization)

Optimizing Fine-Tuning with LoRA

Hyperparameter tuning for LoRA
Evaluating model performance
Minimizing resource consumption

Hands-On Labs

Fine-tuning BERT with LoRA for text classification
Applying LoRA to T5 for summarization tasks
Exploring custom LoRA configurations for unique tasks

Deploying LoRA-Tuned Models

Exporting and saving LoRA-tuned models
Integrating LoRA models into applications
Deploying models in production environments

Advanced Techniques in LoRA

Combining LoRA with other optimization methods
Scaling LoRA for larger models and datasets
Exploring multimodal applications with LoRA

Challenges and Best Practices

Avoiding overfitting with LoRA
Ensuring reproducibility in experiments
Strategies for troubleshooting and debugging

Future Trends in Efficient Fine-Tuning

Emerging innovations in LoRA and related methods
Applications of LoRA in real-world AI
Impact of efficient fine-tuning on AI development

Summary and Next Steps

Requirements

Basic understanding of machine learning concepts
Familiarity with Python programming
Experience with deep learning frameworks like TensorFlow or PyTorch

Audience

Developers
AI practitioners

14 Hours

Delivery Options

Private Group Training

Our identity is rooted in delivering exactly what our clients need.

Pre-course call with your trainer
Customisation of the learning experience to achieve your goals -

Bespoke outlines
Practical hands-on exercises containing data / scenarios recognisable to the learners

Training scheduled on a date of your choice
Delivered online, onsite/classroom or hybrid by experts sharing real world experience

Private Group Prices RRP from £3800 online delivery, based on a group of 2 delegates, £1200 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.

Efficient Fine-Tuning with Low-Rank Adaptation (LoRA) Training Course

Course Outline

Requirements

Delivery Options

Private Group Training

Public Training

Provisional Upcoming Courses (Contact Us For More Information)

Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

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This site in other countries/regions

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Efficient Fine-Tuning with Low-Rank Adaptation (LoRA) Training Course

Course Outline

Requirements

Delivery Options

Private Group Training

Public Training

Provisional Upcoming Courses (Contact Us For More Information)

Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

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Prompt Engineering and Few-Shot Fine-Tuning

Introduction to Transfer Learning

Troubleshooting Fine-Tuning Challenges

Related Categories

Fine-Tuning

This site in other countries/regions

Europe

Asia Pacific

North America

South America

Africa / Middle East

Other sites