Fine-Tuning Models and Large Language Models (LLMs) Training Course
Fine-tuning models and LLMs represents a vital process for adapting pre-trained machine learning models to specific tasks and datasets. This course delves into the techniques, tools, and best practices associated with fine-tuning, emphasizing practical implementations and optimization strategies to achieve high performance.
This instructor-led, live training (available online or onsite) is designed for intermediate to advanced-level professionals seeking to customize pre-trained models for specific tasks and datasets.
By the conclusion of this training, participants will be able to:
- Grasp the principles of fine-tuning and its diverse applications.
- Prepare datasets effectively for fine-tuning pre-trained models.
- Apply fine-tuning techniques to large language models (LLMs) for NLP tasks.
- Optimize model performance and address common challenges.
Format of the Course
- Interactive lectures and discussions.
- Extensive exercises and practice opportunities.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request customized training for this course, please contact us to arrange.
Course Outline
Introduction to Fine-Tuning
- What is fine-tuning?
- Use cases and benefits of fine-tuning
- Overview of pre-trained models and transfer learning
Preparing for Fine-Tuning
- Collecting and cleaning datasets
- Understanding task-specific data requirements
- Exploratory data analysis and preprocessing
Fine-Tuning Techniques
- Transfer learning and feature extraction
- Fine-tuning transformers with Hugging Face
- Fine-tuning for supervised vs unsupervised tasks
Fine-Tuning Large Language Models (LLMs)
- Adapting LLMs for NLP tasks (e.g., text classification, summarization)
- Training LLMs with custom datasets
- Controlling LLM behavior with prompt engineering
Optimization and Evaluation
- Hyperparameter tuning
- Evaluating model performance
- Addressing overfitting and underfitting
Scaling Fine-Tuning Efforts
- Fine-tuning on distributed systems
- Leveraging cloud-based solutions for scalability
- Case studies: Large-scale fine-tuning projects
Best Practices and Challenges
- Best practices for fine-tuning success
- Common challenges and troubleshooting
- Ethical considerations in fine-tuning AI models
Advanced Topics (Optional)
- Fine-tuning multi-modal models
- Zero-shot and few-shot learning
- Exploring LoRA (Low-Rank Adaptation) techniques
Summary and Next Steps
Requirements
- Understanding of machine learning fundamentals
- Experience with Python programming
- Familiarity with pre-trained models and their applications
Audience
- Data scientists
- Machine learning engineers
- AI researchers
Open Training Courses require 5+ participants.
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