vertex-tuning-gemini

Fine-tune Gemini models on Vertex AI with JSONL data and Python scripts.

775|303|Updated May 27, 2021

Search Tags:#llm #fine-tuning #gemini #generative ai #vertex ai #model customization

Ready to run

npx skills add https://github.com/GoogleCloudPlatform/vertex-ai-samples --skill vertex-tuning-gemini

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: vertex-tuning-gemini
Source: https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/skills/vertex-tuning/gemini
Command: npx skills add https://github.com/GoogleCloudPlatform/vertex-ai-samples --skill vertex-tuning-gemini

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires gcloud, python3, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of fine-tuning Google's Gemini Large Language Models using Vertex AI's managed infrastructure, enabling users to customize model behavior for specific tasks without deep ML engineering expertise.

Core Features & Use Cases

End-to-End Workflow: Guides users through environment setup, data preparation, job submission, monitoring, and deployment.
Data Formatting & Validation: Includes scripts to convert and validate datasets into the required JSONL format.
Model Configuration: Assists in selecting Gemini models and configuring hyperparameters for tuning.
Use Case: A company wants to fine-tune a Gemini model to better understand and respond to customer support queries specific to their product catalog. This Skill provides the necessary steps and tools to achieve that.

Quick Start

Use the vertex-tuning-gemini skill to prepare your dataset and submit a fine-tuning job for a Gemini model.

Frequently Asked Questions about vertex-tuning-gemini

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I fine-tune a Gemini model on Vertex AI?▼

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.

What data format is required for Gemini fine-tuning on Vertex AI?▼

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.

Do I need specific IAM permissions to tune Gemini models on Vertex AI?▼

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.

Can I customize Gemini model behavior for customer support queries without deep ML expertise?▼

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.

What dependencies are needed to run the vertex-tuning-gemini skill?▼

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.

Does this skill handle deploying the fine-tuned Gemini model after the job completes?▼

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.