Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs Training Course
Parameter-Efficient Fine-Tuning (PEFT) comprises a suite of techniques designed to facilitate the efficient adaptation of large language models (LLMs) by updating only a small fraction of the model's parameters.
This instructor-led live training, available either online or at your location, is tailored for intermediate-level data scientists and AI engineers seeking to fine-tune large language models in a more cost-effective and efficient manner through methods such as LoRA, Adapter Tuning, and Prefix Tuning.
Upon completing this training, participants will be capable of:
- Grasping the theoretical foundations of parameter-efficient fine-tuning approaches.
- Implementing LoRA, Adapter Tuning, and Prefix Tuning utilizing the Hugging Face PEFT library.
- Evaluating the performance and cost trade-offs between PEFT methods and 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 sessions.
- Hands-on implementation within a live laboratory environment.
Customization Options
- To request a customized training program for this course, please contact us to arrange the details.
Course Outline
Introduction to Parameter-Efficient Fine-Tuning (PEFT)
- Motivation and limitations of full fine-tuning
- Overview of PEFT: goals and benefits
- Industry applications and use cases
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: Conducting Prefix Tuning on an LLM task
Evaluating and Comparing PEFT Methods
- Metrics for assessing performance and efficiency
- Trade-offs in training speed, memory usage, and accuracy
- Conducting benchmarking experiments and interpreting results
Deploying Fine-Tuned Models
- Saving and loading fine-tuned models
- Deployment considerations for PEFT-based models
- Integrating models 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
- A solid understanding of machine learning fundamentals
- Practical experience working with large language models (LLMs)
- Familiarity with Python and PyTorch
Target Audience
- Data scientists
- AI engineers
Open Training Courses require 5+ participants.
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