Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs Training Course
Parameter-Efficient Fine-Tuning (PEFT) comprises a set of strategies that allow for the efficient adaptation of large language models (LLMs) by adjusting only a limited portion of their parameters.
This instructor-led, live training session (available online or on-site) is designed for data scientists and AI engineers with an intermediate level of expertise who aim to fine-tune large language models in a more cost-effective and efficient manner using techniques such as LoRA, Adapter Tuning, and Prefix Tuning.
Upon completion of this training, participants will be capable of:
- Gaining a clear understanding of the theoretical foundations behind parameter-efficient fine-tuning methods.
- Implementing LoRA, Adapter Tuning, and Prefix Tuning leveraging the Hugging Face PEFT library.
- Analyzing the performance and cost trade-offs between PEFT methods and full fine-tuning approaches.
- Deploying and scaling fine-tuned LLMs while significantly reducing computational and storage demands.
Course Format
- Interactive lectures and group discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live laboratory environment.
Customization Options
- To arrange a customized training session for this course, please reach out to us.
Course Outline
Introduction to Parameter-Efficient Fine-Tuning (PEFT)
- Motivation and constraints of full fine-tuning.
- Overview of PEFT: objectives and advantages.
- Industry applications and use cases.
LoRA (Low-Rank Adaptation)
- Core concepts and intuition behind LoRA.
- Implementing LoRA with Hugging Face and PyTorch.
- Practical exercise: Fine-tuning a model using LoRA.
Adapter Tuning
- Functionality of adapter modules.
- Integration with transformer-based architectures.
- Practical exercise: Applying Adapter Tuning to a transformer model.
Prefix Tuning
- Utilizing soft prompts for fine-tuning.
- Strengths and limitations relative to LoRA and adapters.
- Practical exercise: Implementing Prefix Tuning on an LLM task.
Evaluating and Comparing PEFT Methods
- Key metrics for assessing performance and efficiency.
- Trade-offs regarding training speed, memory usage, and accuracy.
- Conducting benchmarking experiments and interpreting results.
Deploying Fine-Tuned Models
- Procedures for saving and loading fine-tuned models.
- Key considerations for deploying PEFT-based models.
- Integration into applications and data pipelines.
Best Practices and Extensions
- Combining PEFT with quantization and model distillation.
- Application in low-resource and multilingual environments.
- Future trends and active research areas.
Summary and Next Steps
Requirements
- Foundational knowledge of machine learning concepts.
- 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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