Fine-Tuning Models and Large Language Models (LLMs) Training Course
Refining pre-trained machine learning models and Large Language Models (LLMs) is a critical process for tailoring these systems to specific tasks and datasets. This course delves into the techniques, tools, and best practices for model refinement, with a focus on practical implementation and optimization strategies to achieve superior performance.
This instructor-led, live training (available online or onsite) is designed for intermediate to advanced-level professionals who want to customize pre-trained models for specific tasks and datasets.
By the end of this training, participants will be able to:
- Grasp the principles of model refinement and its real-world applications.
- Prepare datasets effectively for refining pre-trained models.
- Refine Large Language Models (LLMs) for Natural Language Processing (NLP) tasks.
- Optimize model performance and navigate common challenges.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation within a live-lab environment.
Course Customization Options
- To arrange customized training for this course, please contact us.
Course Outline
Introduction to Model Refinement
- Understanding what model refinement entails
- Use cases and benefits of refinement
- Overview of pre-trained models and transfer learning
Preparing for Refinement
- Collecting and cleaning datasets
- Understanding task-specific data requirements
- Exploratory data analysis and preprocessing
Refinement Techniques
- Transfer learning and feature extraction
- Refining transformers using Hugging Face
- Refinement approaches for supervised versus unsupervised tasks
Refining Large Language Models (LLMs)
- Adapting LLMs for NLP tasks (e.g., text classification, summarization)
- Training LLMs with custom datasets
- Governing LLM behavior through prompt engineering
Optimization and Evaluation
- Hyperparameter tuning
- Evaluating model performance
- Addressing overfitting and underfitting
Scaling Refinement Efforts
- Refining on distributed systems
- Leveraging cloud-based solutions for scalability
- Case studies: Large-scale refinement projects
Best Practices and Challenges
- Best practices for successful refinement
- Common challenges and troubleshooting
- Ethical considerations in refining AI models
Advanced Topics (Optional)
- Refining multi-modal models
- Zero-shot and few-shot learning
- Exploring LoRA (Low-Rank Adaptation) techniques
Summary and Next Steps
Requirements
- Foundational understanding of machine learning concepts
- Proficiency in Python programming
- Familiarity with pre-trained models and their applications
Audience
- Data scientists
- Machine learning engineers
- AI researchers
Need help picking the right course?
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Fine-Tuning Models and Large Language Models (LLMs) Training Course - Enquiry
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