Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Fundamentals of Model Fine-Tuning with Ollama
- Recognizing the necessity for AI model refinement
- Key advantages of customization for targeted applications
- Overview of Ollama’s fine-tuning features
Configuring the Fine-Tuning Workspace
- Setting up Ollama for AI model adaptation
- Installing essential libraries (PyTorch, Hugging Face, etc.)
- Leveraging GPU acceleration for hardware efficiency
Curating Datasets for Refinement
- Data gathering, purification, and preprocessing
- Labeling and annotation methods
- Best practices for partitioning data (training, validation, testing)
Executing AI Model Fine-Tuning on Ollama
- Selecting appropriate pre-trained models for customization
- Hyperparameter adjustment and optimization tactics
- Workflows for text generation, classification, and additional tasks
Assessing and Enhancing Model Outcomes
- Metrics for evaluating model precision and resilience
- Mitigating bias and overfitting challenges
- Performance testing and iterative improvement
Rolling Out Custom AI Models
- Exporting and integrating refined models
- Scaling models for live production systems
- Maintaining compliance and security during deployment
Sophisticated Methods for Model Adaptation
- Utilizing reinforcement learning to enhance AI models
- Implementing domain adaptation strategies
- Investigating model compression for improved efficiency
Emerging Trends in AI Model Customization
- New developments in fine-tuning approaches
- Progress in training AI models with limited resources
- The influence of open-source AI on enterprise integration
Recap and Forward-Looking Steps
Requirements
- Profound knowledge of deep learning and LLM architectures
- Proficiency in Python and relevant AI frameworks
- Solid grasp of dataset curation and model training processes
Target Learners
- AI researchers investigating model refinement techniques
- Data scientists optimizing AI models for dedicated tasks
- LLM developers creating specialized language models
