Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs Training Course
Parameter-Efficient Fine-Tuning (PEFT) comprises a suite of methodologies designed to adapt large language models (LLMs) effectively by adjusting only a minimal portion of their parameters.
This live, instructor-led training program, available either online or on-site, targets intermediate-level data scientists and AI professionals seeking to fine-tune LLMs with greater cost-effectiveness and efficiency through techniques such as LoRA, Adapter Tuning, and Prefix Tuning.
Upon completion of this course, participants will be equipped to:
- Grasp the theoretical foundations underpinning parameter-efficient fine-tuning strategies.
- Apply LoRA, Adapter Tuning, and Prefix Tuning frameworks using Hugging Face PEFT.
- Analyze the performance versus cost trade-offs between PEFT techniques and traditional full fine-tuning.
- Deploy and scale fine-tuned LLMs while significantly reducing compute and storage demands.
Course Format
- Engaging interactive lectures and facilitated discussions.
- Extensive practical exercises and reinforcement activities.
- Live, hands-on implementation within a dedicated lab environment.
Customization Opportunities
- Reach out to our team to discuss and arrange a tailored training experience specific to your needs.
Course Outline
Foundations of Parameter-Efficient Fine-Tuning (PEFT)
- Drivers and constraints associated with full fine-tuning
- Overview of PEFT objectives and strategic advantages
- Real-world industry applications and use cases
LoRA (Low-Rank Adaptation)
- Theoretical concepts and intuitive understanding of LoRA
- Practical implementation of LoRA via Hugging Face and PyTorch
- Live workshop: Fine-tuning a model using LoRA
Adapter Tuning
- Mechanisms and functionality of adapter modules
- Integration strategies for transformer-based architectures
- Live workshop: Applying Adapter Tuning to a transformer model
Prefix Tuning
- Leveraging soft prompts for the fine-tuning process
- Analyzing strengths and limitations relative to LoRA and adapters
- Live workshop: Implementing Prefix Tuning for an LLM task
Assessment and Comparative Analysis of PEFT Methods
- Key metrics for gauging model performance and efficiency
- Trade-offs regarding training speed, memory consumption, and accuracy
- Interpreting benchmarking experiments and experimental results
Deployment Strategies for Fine-Tuned Models
- Procedures for saving and loading fine-tuned weights
- Key considerations for deploying PEFT-based solutions
- Seamless integration into existing applications and pipelines
Best Practices and Advanced Extensions
- Combining PEFT with quantization and knowledge distillation
- Application in low-resource and multilingual contexts
- Emerging trends and active areas of research
Requirements
- Solid understanding of core machine learning principles
- Practical experience with large language models (LLMs)
- Proficiency in Python and PyTorch
Target Audience
- Data Scientists
- AI Engineers
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs Training Course - Enquiry
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