Fine-Tuning and Customizing AI Models on Ollama

• 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

Ollama provides a powerful platform for fine-tuning and customizing AI models locally, allowing professionals to tailor large language models (LLMs) to their specific use cases.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to fine-tune and customize AI models on Ollama for enhanced performance and domain-specific applications.

By the end of this training, participants will be able to:

• Set up an efficient environment for fine-tuning AI models on Ollama.
• Prepare datasets for supervised fine-tuning and reinforcement learning.
• Optimize AI models for performance, accuracy, and efficiency.
• Deploy customized models in production environments.
• Evaluate model improvements and ensure robustness.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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