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Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs Počítačový Kurz

Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs Počítačový Kurz

Parameter-Efficient Fine-Tuning (PEFT) is a collection of techniques that enable efficient adaptation of large language models (LLMs) by modifying only a small subset of parameters.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists and AI engineers who wish to fine-tune large language models more affordably and efficiently using methods like LoRA, Adapter Tuning, and Prefix Tuning.

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

Understand the theory behind parameter-efficient fine-tuning approaches.
Implement LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT.
Compare performance and cost trade-offs of PEFT methods vs. full fine-tuning.
Deploy and scale fine-tuned LLMs with reduced compute and storage requirements.

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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Návrh Školení

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

Motivation and limitations of full fine-tuning
Overview of PEFT: goals and benefits
Applications and use cases in industry

LoRA (Low-Rank Adaptation)

Concept and intuition behind LoRA
Implementing LoRA using Hugging Face and PyTorch
Hands-on: Fine-tuning a model with LoRA

Adapter Tuning

How adapter modules work
Integration with transformer-based models
Hands-on: Applying Adapter Tuning to a transformer model

Prefix Tuning

Using soft prompts for fine-tuning
Strengths and limitations compared to LoRA and adapters
Hands-on: Prefix Tuning on an LLM task

Evaluating and Comparing PEFT Methods

Metrics for evaluating performance and efficiency
Trade-offs in training speed, memory usage, and accuracy
Benchmarking experiments and result interpretation

Deploying Fine-Tuned Models

Saving and loading fine-tuned models
Deployment considerations for PEFT-based models
Integrating into applications and pipelines

Best Practices and Extensions

Combining PEFT with quantization and distillation
Use in low-resource and multilingual settings
Future directions and active research areas

Summary and Next Steps

Požadavky

An understanding of machine learning fundamentals
Experience working with large language models (LLMs)
Familiarity with Python and PyTorch

Audience

Data scientists
AI engineers

14 hodiny

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