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Course Outline
Introduction to QLoRA and Quantization
- Overview of quantization and its role in model optimization
- Introduction to QLoRA framework and its benefits
- Key differences between QLoRA and traditional fine-tuning methods
Fundamentals of Large Language Models (LLMs)
- Introduction to LLMs and their architecture
- Challenges of fine-tuning large models at scale
- How quantization helps overcome computational constraints in LLM fine-tuning
Implementing QLoRA for Fine-Tuning LLMs
- Setting up the QLoRA framework and environment
- Preparing datasets for QLoRA fine-tuning
- Step-by-step guide to implementing QLoRA on LLMs using Python and PyTorch/TensorFlow
Optimizing Fine-Tuning Performance with QLoRA
- How to balance model accuracy and performance with quantization
- Techniques for reducing compute costs and memory usage during fine-tuning
- Strategies for fine-tuning with minimal hardware requirements
Evaluating Fine-Tuned Models
- How to assess the effectiveness of fine-tuned models
- Common evaluation metrics for language models
- Optimizing model performance post-tuning and troubleshooting issues
Deploying and Scaling Fine-Tuned Models
- Best practices for deploying quantized LLMs into production environments
- Scaling deployment to handle real-time requests
- Tools and frameworks for model deployment and monitoring
Real-World Use Cases and Case Studies
- Case study: Fine-tuning LLMs for customer support and NLP tasks
- Examples of fine-tuning LLMs in various industries like healthcare, finance, and e-commerce
- Lessons learned from real-world deployments of QLoRA-based models
Summary and Next Steps
Requirements
- An understanding of machine learning fundamentals and neural networks
- Experience with model fine-tuning and transfer learning
- Familiarity with large language models (LLMs) and deep learning frameworks (e.g., PyTorch, TensorFlow)
Audience
- Machine learning engineers
- AI developers
- Data scientists
14 Hours
