Why do some models have 'Open Weights' and others 'Closed Weights'?

Why do some models have 'Open Weights' i.e open to public and others are 'Closed weights' and how does this effect fine-tuning?

The terms "Open Weights" and "Closed Weights" in the context of machine learning models refer to whether the trained weights of a model are publicly available or kept private. This distinction can significantly impact how models are used, particularly in fine-tuning.

Open Weights

1. Definition: Open weight models are those where the trained model weights are publicly available. Examples include many of the models released by organizations like OpenAI (e.g., some versions of GPT), Google's BERT, etc.

2. Advantages:

   • Accessibility: Anyone can download and use these models, which democratizes access to state-of-the-art AI technologies.

   • Fine-Tuning: Open weights allow for easier fine-tuning on specific tasks or datasets. Users can start with a powerful pre-trained model and adapt it to their needs without needing vast resources for training from scratch.

   • Research and Innovation: They foster academic research and innovation, as researchers can build upon existing models to explore new ideas.

3. Considerations:

   • Ethical and Misuse Risks: Open models can be used by anyone, which raises concerns about misuse, such as generating harmful or biased content.

Closed Weights

1. Definition: Closed weight models are proprietary, with their weights kept private and not available to the public. This is common in commercial applications where a company has invested significant resources in training a model.

2. Advantages:

   • Competitive Advantage: Keeping model weights private can offer competitive advantages and protect intellectual property.

   • Control over Use: It allows the owning entity to control how the model is used, potentially reducing the risk of misuse.

3. Disadvantages:

   • Limited Accessibility: The broader community cannot access these models for learning, research, or application development.

   • Fine-Tuning Limitations: Without access to the model weights, external parties cannot fine-tune the model on their specific tasks or datasets.

Impact on Fine-Tuning

• Open Weights: You can directly fine-tune these models on your data, which is particularly beneficial for tasks that require domain-specific knowledge that the original model training may not have included. This is common in academic research and smaller companies that may not have the resources for full-scale model training.

• Closed Weights: Fine-tuning is not possible unless you have access to the model, which is typically restricted to the organization that developed it or its partners. In this case, you might rely on API access to the model, but this only allows you to use the model as-is, without fine-tuning.

Conclusion

Whether a model has open or closed weights affects how it can be used, especially regarding fine-tuning and accessibility. Open models promote wider use and innovation but come with potential risks of misuse. Closed models offer control and competitive advantage but limit the ability of the external community to build upon or fine-tune them.