how to do fine tuning in Gemini API model

[ManishUmrania]

<p>
I'm building a custom generative AI application using Vertex AI's Gemini API for my organization. The goal is to generate SQL or XML objects based on input.
</p>
<p>
I'm currently immersed in the exciting world of fine-tuning language models (LLMs) and I'm seeking guidance on the most effective strategies for evaluating, monitoring, and retraining Vertex AI's gemini API. As I delve deeper into this process, I've encountered various challenges and uncertainties, prompting me to reach out to this knowledgeable community for insights and advice. Fine-tuning Gemini API involves adapting a pre-trained model to a specific task or domain, allowing it to generate more relevant and contextually appropriate outputs. However, ensuring the effectiveness of this fine-tuning process requires careful evaluation and monitoring at every stage. I have fine-tuned the pre-trained model which contains multiple AI skills like text to SQL conversion, text to XML conversion, and product information. My question is about the best practice to create and manage the dataset for these multiple skills using a single Gemini model. After fine-tuning, generally, we evaluate and examine the model output. My question is around re-tuning the model again for the mistakes it is making with the previous dataset. For further fine-tuning, do we need to keep the dataset we used for the previous fine-tuning and append the new dataset or as the model is already trained with the previous dataset and we just need to keep the data for the mistakes it is making?
</p>
<p>
Moreover, How frequently should a model be retrained to maintain its relevance and accuracy? Are there specific triggers or indicators that signal the need for retraining, such as changes in data distribution or task requirements?
</p>
<p>
One aspect I'm particularly interested in is the evaluation criteria for assessing the performance of a Gemini AI model. What metrics or benchmarks should be considered to determine the quality of the model's outputs? Are there specific evaluation techniques that have proven to be particularly reliable or informative in this context?
</p>

[catherinwilliam]

To fine-tune the Gemini API model effectively for generating SQL or XML objects, follow these steps:

1. Dataset Preparation: Create a combined dataset that includes examples for each skill (text-to-SQL, text-to-XML, product information). Ensure the dataset is balanced and representative of the tasks.

2. Fine-Tuning: Fine-tune the pre-trained model using this comprehensive dataset. Monitor the model's performance during training to avoid overfitting.

3. Evaluation and Monitoring: After fine-tuning, evaluate the model's output using relevant metrics like accuracy, precision, recall, and F1 score. Regularly monitor the model's performance to detect any drop in accuracy or relevance.

4. Retuning and Data Management: If the model makes mistakes, gather these error cases and add them to your dataset. When retraining, include both the original and the new datasets to reinforce previous learning while correcting mistakes.

5. Retraining Frequency: Retrain the model whenever you notice significant changes in data distribution, task requirements, or a decline in model performance. Regularly scheduled evaluations can help determine the optimal retraining intervals.

6. Evaluation Criteria: Use metrics that align with your specific tasks. For SQL and XML generation, consider the correctness of the syntax and the relevance of the generated content. Conduct qualitative reviews and user feedback sessions to supplement quantitative metrics.

By following these strategies, you can ensure your Gemini API model remains accurate, relevant, and effective for generating SQL or XML objects.