Reminder: Comparing Fine-tuned Model vs. Base Model

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Fine-tuning is a powerful technique for improving an LLM's performance on specific tasks or lowering inference costs.

However, before deploying a fine-tuned model to production, an important question remains:

Is this model actually better than the one currently in use?

In this session, we'll demonstrate how to fine-tune a base model using the Together Python Library and compare its performance against a base model using Autoblocks AI's testing and evaluation solution.

As always, thanks for reading, and hope you have a great day ahead ✌️

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