Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs Training Course
Parameter-Efficient Fine-Tuning (PEFT) encompasses a set of techniques designed to adapt large language models (LLMs) efficiently by altering only a limited number of parameters.
This instructor-led, live training, available online or onsite, targets intermediate-level data scientists and AI engineers looking to fine-tune large language models in a cost-effective and efficient manner using techniques such as LoRA, Adapter Tuning, and Prefix Tuning.
Upon completing this training, participants will be capable of:
- Gaining insight into the theoretical foundations of parameter-efficient fine-tuning strategies.
- Applying LoRA, Adapter Tuning, and Prefix Tuning using the Hugging Face PEFT library.
- Evaluating the performance and cost trade-offs of PEFT methods in comparison to full fine-tuning.
- Deploying and scaling fine-tuned LLMs while minimizing compute and storage demands.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical applications.
- Live-lab environment for hands-on implementation.
Customization Options
- For tailored training arrangements, please contact us to discuss your needs.
Course Outline
Introduction to Parameter-Efficient Fine-Tuning (PEFT)
- Understanding the motivation and limitations of full fine-tuning
- Overview of PEFT: objectives and advantages
- Industry applications and use cases
LoRA (Low-Rank Adaptation)
- Core concepts and intuitive understanding of LoRA
- Implementing LoRA with Hugging Face and PyTorch
- Practical session: Fine-tuning a model using LoRA
Adapter Tuning
- Mechanics of adapter modules
- Integration with transformer-based architectures
- Practical session: Applying Adapter Tuning to a transformer model
Prefix Tuning
- Leveraging soft prompts for fine-tuning
- Advantages and limitations relative to LoRA and adapters
- Practical session: Executing Prefix Tuning on an LLM task
Evaluating and Comparing PEFT Methods
- Key metrics for assessing performance and efficiency
- Trade-offs regarding training speed, memory consumption, and accuracy
- Benchmarking experiments and interpreting results
Deploying Fine-Tuned Models
- Techniques for saving and loading fine-tuned models
- Considerations for deploying PEFT-based models
- Integration into applications and data pipelines
Best Practices and Extensions
- Combining PEFT with quantization and distillation techniques
- Application in low-resource and multilingual environments
- Emerging trends and active research areas
Summary and Next Steps
Requirements
- Foundational knowledge of machine learning concepts
- Practical experience with large language models (LLMs)
- Proficiency in Python and PyTorch
Target Audience
- Data Scientists
- AI Engineers
Open Training Courses require 5+ participants.
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