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Course Outline
Introduction to Model Fine-Tuning on Ollama
- Understanding the need for fine-tuning AI models
- Key benefits of customization for specific applications
- Overview of Ollama’s capabilities for fine-tuning
Setting Up the Fine-Tuning Environment
- Configuring Ollama for AI model customization
- Installing required frameworks (PyTorch, Hugging Face, etc.)
- Ensuring hardware optimization with GPU acceleration
Preparing Datasets for Fine-Tuning
- Data collection, cleaning, and preprocessing
- Labeling and annotation techniques
- Best practices for dataset splitting (training, validation, testing)
Fine-Tuning AI Models on Ollama
- Choosing the right pre-trained models for customization
- Hyperparameter tuning and optimization strategies
- Fine-tuning workflows for text generation, classification, and more
Evaluating and Optimizing Model Performance
- Metrics for assessing model accuracy and robustness
- Addressing bias and overfitting issues
- Performance benchmarking and iteration
Deploying Customized AI Models
- Exporting and integrating fine-tuned models
- Scaling models for production environments
- Ensuring compliance and security in deployment
Advanced Techniques for Model Customization
- Using reinforcement learning for AI model improvements
- Applying domain adaptation techniques
- Exploring model compression for efficiency
Future Trends in AI Model Customization
- Emerging innovations in fine-tuning methodologies
- Advancements in low-resource AI model training
- Impact of open-source AI on enterprise adoption
Summary and Next Steps
Requirements
- Strong understanding of deep learning and LLMs
- Experience with Python programming and AI frameworks
- Familiarity with dataset preparation and model training
Audience
- AI researchers exploring model fine-tuning
- Data scientists optimizing AI models for specific tasks
- LLM developers building customized language models
14 Hours
