vertex-tuning-gemini

You fine-tune a Gemini model by preparing a JSONL dataset, uploading it to Cloud Storage, configuring hyperparameters, and submitting a tuning job via Python scripts. This Skill manages the entire lifecycle from environment setup to deployment.

Gemini fine-tuning on Vertex AI requires datasets to be in JSONL format. The skill includes scripts to convert and validate your data into this specific structure before uploading it to Google Cloud Storage for the tuning job.

Yes, tuning Gemini models requires specific IAM permissions and API enablement for both Vertex AI and Cloud Storage. You must configure these access controls in Google Cloud before submitting any fine-tuning jobs.

Yes, you can customize Gemini models for specific tasks like customer support queries without deep ML expertise. This skill streamlines the process using Vertex AI's managed infrastructure, guiding you through data preparation, job submission, and deployment.

To run the vertex-tuning-gemini skill, you need gcloud and python3 installed in your environment. These dependencies are required to execute the scripts that configure and submit the fine-tuning jobs to Google Cloud Vertex AI.

Yes, the skill manages the end-to-end workflow including model deployment. After your Vertex AI fine-tuning job completes, it guides you through the process of deploying the customized Gemini model for inference.