Open Models: The Secret Weapon for Next-Generation Software by Remigiusz Samborski

Open models like Gemma provide access to both model weights and architecture, allowing complete customization and modification unlike closed API-only models

Key benefits of open models include:

• On-premise/edge deployment capabilities
• Privacy and data control
• No cloud costs for inference
• Ability to fine-tune for specific use cases
• Different model sizes for various resource constraints

Gemma comes in multiple variants:

• Base models (2B, 7B, 27B parameters)
• Instruction-tuned versions
• Specialized versions (Code Gemma, Data Gemma, Poly Gemma)
• Quantized versions (INT8, INT4) for efficiency

Deployment options include:

• Google Cloud Vertex AI
• Local deployment
• Edge devices/mobile
• Kubernetes clusters
• Integration with frameworks like Keras and MediaPipe

The Open Source AI Definition is being developed to standardize what constitutes an “open” AI model, with 17 components covering aspects like:

• Model weights accessibility
• Training code availability
• Data transparency
• Security considerations
• Privacy requirements

Community innovation and fine-tuning capabilities are driving rapid expansion of use cases and specialized implementations

Models can be deployed offline with no internet connectivity required, making them suitable for sensitive or disconnected environments

Smaller models trade capability for faster inference and lower resource requirements, allowing deployment flexibility based on use case needs

Integration with existing ML frameworks and tools enables rapid prototyping and development

Key considerations for adoption include:

• Model size vs capability requirements
• Resource constraints
• Privacy needs
• Fine-tuning requirements
• Deployment environment