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Course Outline
Introduction to Parameter-Efficient Fine-Tuning (PEFT)
- Motivation and limitations of full fine-tuning
- Overview of PEFT: goals and benefits
- Applications and use cases in industry
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 work
- Integration with transformer-based models
- Hands-on: Applying Adapter Tuning to a transformer model
Prefix Tuning
- Using soft prompts for fine-tuning
- Strengths and limitations compared to LoRA and adapters
- Hands-on: Prefix Tuning on an LLM task
Evaluating and Comparing PEFT Methods
- Metrics for evaluating performance and efficiency
- Trade-offs in training speed, memory usage, and accuracy
- Benchmarking experiments and result interpretation
Deploying Fine-Tuned Models
- Saving and loading fine-tuned models
- Deployment considerations for PEFT-based models
- Integrating into applications and pipelines
Best Practices and Extensions
- Combining PEFT with quantization and distillation
- Use in low-resource and multilingual settings
- Future directions and active research areas
Summary and Next Steps
Requirements
- An understanding of machine learning fundamentals
- Experience working with large language models (LLMs)
- Familiarity with Python and PyTorch
Audience
- Data scientists
- AI engineers
14 Hours
Delivery Options
Private Group Training
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- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €4560 online delivery, based on a group of 2 delegates, €1440 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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