Fine-Tuning Large Language Models Using QLoRA Training Course
This instructor-led, live training (online or onsite) is designed for intermediate to advanced 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 in a government context.
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 relevant to government operations.
- Optimize fine-tuning performance on limited computational resources using quantization methods, ensuring efficiency for government use cases.
- Deploy and evaluate fine-tuned models in real-world government applications efficiently.
Format of the Course
- Interactive lecture and discussion focused on practical applications for government.
- Lots of exercises and practice to reinforce learning for government scenarios.
- Hands-on implementation in a live-lab environment tailored to government needs.
Course Customization Options
- To request a customized training for this course, specifically designed to meet the unique requirements of your government agency, please contact us to arrange.
Course Outline
Introduction to QLoRA and Quantization for Government
- Overview of quantization and its role in optimizing model performance
- Introduction to the QLoRA framework and its benefits for government applications
- Key differences between QLoRA and traditional fine-tuning methods in a public sector context
Fundamentals of Large Language Models (LLMs) for Government
- Introduction to LLMs and their architecture, tailored for government use cases
- Challenges of fine-tuning large models at scale within government environments
- How quantization helps overcome computational constraints in LLM fine-tuning for government operations
Implementing QLoRA for Fine-Tuning LLMs for Government
- Setting up the QLoRA framework and environment for government systems
- Preparing datasets for QLoRA fine-tuning to meet public sector standards
- Step-by-step guide to implementing QLoRA on LLMs using Python and PyTorch/TensorFlow, adapted for government workflows
Optimizing Fine-Tuning Performance with QLoRA for Government
- Strategies for balancing model accuracy and performance with quantization in a government setting
- Techniques for reducing compute costs and memory usage during fine-tuning, aligned with public sector budgets
- Approaches to fine-tuning with minimal hardware requirements, suitable for government IT infrastructure
Evaluating Fine-Tuned Models for Government
- Methods for assessing the effectiveness of fine-tuned models in a government context
- Common evaluation metrics for language models, tailored for public sector applications
- Optimizing model performance post-tuning and troubleshooting issues specific to government use cases
Deploying and Scaling Fine-Tuned Models for Government
- Best practices for deploying quantized LLMs into production environments in the public sector
- Scaling deployment to handle real-time requests in government services
- Tools and frameworks for model deployment and monitoring, ensuring compliance with government regulations
Real-World Use Cases and Case Studies for Government
- Case study: Fine-tuning LLMs for customer support and NLP tasks in government agencies
- Examples of fine-tuning LLMs in various industries like healthcare, finance, and e-commerce, with a focus on public sector applications
- Lessons learned from real-world deployments of QLoRA-based models in government settings
Summary and Next Steps for Government
Requirements
- An understanding of machine learning fundamentals and neural networks for government applications.
- Experience with model fine-tuning and transfer learning in public sector environments.
- Familiarity with large language models (LLMs) and deep learning frameworks (e.g., PyTorch, TensorFlow) for government use.
Audience
- Machine learning engineers for government agencies.
- AI developers working in the public sector.
- Data scientists supporting government initiatives.
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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