ضبط وتخصيص برامج الماجستير في القانون

Adapt pre-trained Large Language Models for specific business contexts and tone.

Pillar

Technology – Platforms, Tools, Infrastructure & Productivity

ملخص

This course guides participants through the process of fine-tuning and customizing large language models (LLMs) to meet the unique needs of their organizations. It covers techniques to tailor models for specific industries, workflows, or brand voices, ensuring more relevant, accurate, and context-aware AI outputs.

Learning Objectives

Participants will be able to:

• Understand the fundamentals of LLM fine-tuning and customization

• Prepare domain-specific datasets for training and evaluation

• Implement fine-tuning workflows using popular frameworks

• Customize model behavior to reflect business tone and style

• Evaluate and optimize fine-tuned models for production use

Target Audience

• AI developers and data scientists

• NLP engineers and machine learning practitioners

• AI product managers and technical leads

• Researchers interested in applied AI customization

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Hands-on coding labs with real datasets

• Guided walkthroughs of fine-tuning techniques

• Group discussions and troubleshooting sessions

Materials Provided

• Sample datasets and preprocessing scripts

• Fine-tuning code templates and notebooks

• Model evaluation and benchmarking tools

• Certificate of completion

Outcomes

• Successfully fine-tune pre-trained LLMs for targeted applications

• Customize language models to align with brand voice and context

• Deploy optimized models with improved relevance and accuracy

• Gain practical experience with industry-standard fine-tuning tools

Outline / Content

Day 1: Fundamentals of Fine-Tuning LLMs

• Introduction to pre-trained LLMs and transfer learning

• Overview of fine-tuning techniques and frameworks

• Data collection and preparation for domain adaptation

Day 2: Implementing Fine-Tuning Workflows

• Setting up training environments and tools

• Running fine-tuning experiments with sample datasets

• Monitoring training progress and troubleshooting

Day 3: Customizing Model Behavior

• Techniques for controlling tone, style, and responses

• Incorporating business-specific knowledge and terminology

• Evaluating model outputs and performance metrics

Day 4: Deployment and Optimization

• Preparing fine-tuned models for production use

• Performance tuning and scalability considerations

• Workshop: Fine-tune and deploy a customized LLM

• Group review and feedback session