Fine-Tuning Large Language Models Using QLoRA

• 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

QLoRA is an advanced technique for fine-tuning large language models (LLMs) by leveraging quantization methods, offering a more efficient way to fine-tune these models without incurring massive computational costs. This training will cover both the theoretical foundations and practical implementation of fine-tuning LLMs using QLoRA.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level machine learning engineers, AI developers, and data scientists who wish to learn how to use QLoRA to efficiently fine-tune large models for specific tasks and customizations.

By the end of this training, participants will be able to:

• Understand the theory behind QLoRA and quantization techniques for LLMs.
• Implement QLoRA in fine-tuning large language models for domain-specific applications.
• Optimize fine-tuning performance on limited computational resources using quantization.
• Deploy and evaluate fine-tuned models in real-world applications efficiently.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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