Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs Training Course
Parameter-Efficient Fine-Tuning (PEFT) encompasses a suite of methods designed to facilitate the efficient adaptation of large language models (LLMs) by adjusting only a limited subset of parameters.
This instructor-led live training, available either online or onsite, targets intermediate-level data scientists and AI engineers who aim to fine-tune large language models in a more cost-effective and efficient manner, employing techniques such as LoRA, Adapter Tuning, and Prefix Tuning.
Upon completion of this training, participants will be capable of:
- Gaining insight into the theoretical foundations of parameter-efficient fine-tuning approaches.
- Implementing LoRA, Adapter Tuning, and Prefix Tuning via Hugging Face PEFT.
- Evaluating the performance and cost trade-offs between PEFT methods and full fine-tuning.
- Deploying and scaling fine-tuned LLMs with diminished compute and storage demands.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live-lab environment.
Customization Options
- For inquiries regarding customized training for this course, please reach out to us to make arrangements.
Course Outline
Introduction to Parameter-Efficient Fine-Tuning (PEFT)
- Motivation and limitations associated with full fine-tuning
- Overview of PEFT: goals and benefits
- Applications and use cases within the industry
LoRA (Low-Rank Adaptation)
- Concept and intuition behind LoRA
- Implementing LoRA using Hugging Face and PyTorch
- Hands-on exercise: Fine-tuning a model with LoRA
Adapter Tuning
- How adapter modules function
- Integration with transformer-based models
- Hands-on exercise: Applying Adapter Tuning to a transformer model
Prefix Tuning
- Utilizing soft prompts for fine-tuning
- Strengths and limitations relative to LoRA and adapters
- Hands-on exercise: Prefix Tuning on an LLM task
Evaluating and Comparing PEFT Methods
- Metrics for assessing performance and efficiency
- Trade-offs concerning training speed, memory usage, and accuracy
- Benchmarking experiments and interpretation of results
Deploying Fine-Tuned Models
- Saving and loading fine-tuned models
- Deployment considerations for PEFT-based models
- Integration 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
- Knowledge of machine learning fundamentals
- Practical experience with large language models (LLMs)
- Proficiency in Python and PyTorch
Target Audience
- Data scientists
- AI engineers
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
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(*The final price may vary depending on the technical specialization of the course, the level of customization, the method of delivery and the number of learners)
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