Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs Training Course
Parameter-Efficient Fine-Tuning (PEFT) encompasses a set of techniques that allow for the efficient adaptation of large language models (LLMs) by altering only a minimal portion of the model's parameters.
This instructor-led, live training—available either online or onsite—is designed for intermediate-level data scientists and AI engineers aiming 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 completion of this training, participants will be capable of:
- Gaining a solid understanding of the theoretical foundations underpinning parameter-efficient fine-tuning strategies.
- Implementing LoRA, Adapter Tuning, and Prefix Tuning utilizing Hugging Face PEFT.
- Evaluating the performance and cost trade-offs associated with PEFT methods in comparison to full fine-tuning.
- Deploying and scaling fine-tuned LLMs while significantly reducing compute and storage demands.
Course Format
- Interactive lectures and discussions.
- Abundant exercises and practical applications.
- Hands-on implementation within a live laboratory environment.
Customization Options
- For those interested in requesting a tailored version of this course, please reach out to us to arrange the details.
Course Outline
Introduction to Parameter-Efficient Fine-Tuning (PEFT)
- Exploring the motivation and limitations associated with full fine-tuning.
- Overview of PEFT: key objectives and benefits.
- Real-world applications and industry use cases.
LoRA (Low-Rank Adaptation)
- Conceptual framework and intuition behind LoRA.
- Implementing LoRA with Hugging Face and PyTorch.
- Practical exercise: Fine-tuning a model using LoRA.
Adapter Tuning
- Understanding how adapter modules function.
- Integration with transformer-based architectures.
- Practical exercise: Applying Adapter Tuning to a transformer model.
Prefix Tuning
- Utilizing soft prompts for the fine-tuning process.
- Evaluating strengths and limitations relative to LoRA and adapters.
- Practical exercise: Executing Prefix Tuning on an LLM task.
Evaluating and Comparing PEFT Methods
- Key metrics for assessing performance and efficiency.
- Trade-offs involving training speed, memory consumption, and accuracy.
- Conducting benchmarking experiments and interpreting results.
Deploying Fine-Tuned Models
- Procedures for saving and loading fine-tuned models.
- Considerations for deploying PEFT-based models.
- Seamless integration into applications and data pipelines.
Best Practices and Extensions
- Combining PEFT with quantization and distillation techniques.
- Application in low-resource and multilingual contexts.
- Emerging trends and active research areas.
Summary and Next Steps
Requirements
- A foundational understanding of machine learning principles
- Practical experience working 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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