Fine-tune Gemma models on custom datasets
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Gemma Fine Tuning is a specialized tool designed to optimize Gemma models for specific tasks and datasets. It enables users to adapt pre-trained models to their unique requirements by fine-tuning them on custom data, enhancing performance and relevance for particular use cases.
What datasets are supported by Gemma Fine Tuning?
Gemma Fine Tuning supports a wide range of dataset formats, including text files, CSV, and JSON, making it versatile for various applications.
Can I customize the fine-tuning process further?
Yes, Gemma Fine Tuning allows users to adjust hyperparameters and settings to tailor the process to their specific needs.
How long does the fine-tuning process take?
The duration depends on the size of your dataset and the complexity of the task. Larger datasets and more extensive fine-tuning may require more time.