Embracing Model Optionality and Private LLMs

In the fast-moving world of artificial intelligence, especially with large language models (LLMs), data scientists face a constantly changing landscape. New models and technologies come out frequently, making it hard to choose and stick with one. How can organizations stay flexible while keeping things secure and high-quality? 

Handling the Rapid Changes in AI with Model Optionality

Data scientists today struggle with the fast pace of AI advancements. Here’s why we suggest a multi-modal platform to help your organization manage rapidly changing environment:

1. Keep Ground Truth Data: Save all your ground truth data, which includes the correct answers provided by human evaluators. This data is valuable for testing new models in the future. For example, if you use a Llama 3-based model now, you can compare it with a new model like Mistral 2 later using the same data.
2. Evaluate Multiple Factors: Don’t just look at the quality of the models. Depending on your needs, you might value cost, speed, or accuracy differently. Sometimes, a model that is cheaper and faster might be better even if it’s a bit less accurate.
3. Make Quick Decisions: With technology changing quickly, it’s important to make informed decisions fast. Having a plan to evaluate and switch to better models helps your organization stay up-to-date.

The Benefits of Private LLMs

While public LLMs like ChatGPT are powerful, there are good reasons to build private LLMs:

1. Security and Compliance: Using public models can risk exposing your data to third parties. Building private LLMs ensures your data stays secure and meets compliance requirements.
2. Better Accuracy: Connecting LLMs to your internal data improves the relevance and accuracy of their outputs. It also avoids problems like model drift, where models change without your control.
3. Cost and Performance: Deploying private LLMs on your own servers gives you control over speed, cost, and performance. You can adjust resources based on usage patterns, saving money.

Conclusion

Managing the fast-changing AI landscape requires balancing new models with security, quality, and cost-efficiency. Datasaur’s approach, with LLM Labs, to model optionality helps maintain flexibility, while being able to access private LLMs for a secure and customizable alternative to public models. By using these strategies, organizations can stay ahead in the AI field and make decisions that fit their needs.

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