Fine-Tuning Models and Large Language Models (LLMs) Training Course
Model refinement and the utilisation of Large Language Models (LLMs) constitute a pivotal process in tailoring pre-trained machine learning models for specific tasks and datasets. This course delves into the methodologies, tools, and best practices for refinement, with a strong emphasis on practical implementation and optimisation strategies to attain superior performance.
This instructor-led, live training (available online or onsite) is designed for intermediate to advanced professionals seeking to customise pre-trained models for distinct tasks and datasets.
Upon completion of this training, participants will be capable of:
- Grasping the fundamental principles of refinement and its applications.
- Preparing datasets for the refinement of pre-trained models.
- Refining Large Language Models (LLMs) for Natural Language Processing (NLP) tasks.
- Optimising model performance and resolving common challenges.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live laboratory environment.
Course Customisation Options
- To request a bespoke training session for this course, please contact us to make arrangements.
Course Outline
Introduction to Refinement
- What constitutes refinement?
- 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 for supervised versus unsupervised tasks
Refining Large Language Models (LLMs)
- Adapting LLMs for NLP tasks (e.g., text classification, summarisation)
- Training LLMs with custom datasets
- Controlling LLM behaviour through prompt engineering
Optimisation 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
- Understanding of machine learning fundamentals
- Experience with Python programming
- Familiarity with pre-trained models and their applications
Target Audience
- Data scientists
- Machine learning engineers
- AI researchers
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Fine-Tuning Models and Large Language Models (LLMs) Training Course - Enquiry
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