Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs Training Course
Parameter-Efficient Fine-Tuning (PEFT) encompasses a set of techniques that facilitate the efficient adaptation of large language models (LLMs) by adjusting only a limited subset of parameters.
This instructor-led, live training (available online or on-site) is designed for intermediate-level data scientists and AI engineers seeking to fine-tune large language models in a more cost-effective and efficient manner, utilizing methods such as LoRA, Adapter Tuning, and Prefix Tuning.
Upon completion of this training, participants will be able to:
- Comprehend the theoretical foundations of parameter-efficient fine-tuning approaches.
- Implement LoRA, Adapter Tuning, and Prefix Tuning using the Hugging Face PEFT library.
- Assess the performance and cost trade-offs between PEFT methods and full fine-tuning.
- Deploy and scale fine-tuned LLMs while significantly reducing compute and storage demands.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live laboratory environment.
Customization Options
- To request a customized training session for this course, please contact us to arrange.
Course Outline
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 function.
- Integration with transformer-based models.
- Hands-on: Applying Adapter Tuning to a transformer model.
Prefix Tuning
- Utilizing 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.
- Application in low-resource and multilingual settings.
- Future directions and active research areas.
Summary and Next Steps
Requirements
- Fundamental understanding of machine learning principles.
- Practical experience working with large language models (LLMs).
- Familiarity with Python and PyTorch.
Audience
- Data scientists.
- AI engineers.
Open Training Courses require 5+ participants.
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