Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Low-Rank Adaptation (LoRA)
- What is LoRA?
- Benefits of LoRA for efficient fine-tuning
- Comparison with traditional fine-tuning methods
Understanding Fine-Tuning Challenges
- Limitations of traditional fine-tuning
- Computational and memory constraints
- Why LoRA is an effective alternative
Setting Up the Environment
- Installing Python and required libraries
- Setting up Hugging Face Transformers and PyTorch
- Exploring LoRA-compatible models
Implementing LoRA
- Overview of LoRA methodology
- Adapting pre-trained models with LoRA
- Fine-tuning for specific tasks (e.g., text classification, summarization)
Optimizing Fine-Tuning with LoRA
- Hyperparameter tuning for LoRA
- Evaluating model performance
- Minimizing resource consumption
Hands-On Labs
- Fine-tuning BERT with LoRA for text classification
- Applying LoRA to T5 for summarization tasks
- Exploring custom LoRA configurations for unique tasks
Deploying LoRA-Tuned Models
- Exporting and saving LoRA-tuned models
- Integrating LoRA models into applications
- Deploying models in production environments
Advanced Techniques in LoRA
- Combining LoRA with other optimization methods
- Scaling LoRA for larger models and datasets
- Exploring multimodal applications with LoRA
Challenges and Best Practices
- Avoiding overfitting with LoRA
- Ensuring reproducibility in experiments
- Strategies for troubleshooting and debugging
Future Trends in Efficient Fine-Tuning
- Emerging innovations in LoRA and related methods
- Applications of LoRA in real-world AI
- Impact of efficient fine-tuning on AI development
Summary and Next Steps
Requirements
- Basic understanding of machine learning concepts
- Familiarity with Python programming
- Experience with deep learning frameworks like TensorFlow or PyTorch
Audience
- Developers
- AI practitioners
14 Hours
