vertex-tuning-open-model

Fine-tuning open-source LLMs on Vertex AI involves managing an end-to-end workflow using Python scripts. The process covers data preparation in JSONL format, model selection, hyperparameter tuning, job submission, monitoring, and deployment to Vertex AI endpoints.

Datasets for Vertex AI LLM tuning must be converted and validated into the JSONL format. This data handling step ensures the conversational or instructional data aligns with the expected input structure for models like Gemma or Qwen.

Yes, you can use PEFT for fine-tuning open models on Google Cloud Vertex AI. The workflow supports both full parameter and PEFT tuning, allowing you to efficiently adapt large language models while managing compute resources.

To deploy a fine-tuned open LLM to a Vertex AI endpoint, you submit a tuning job via Python scripts, monitor the training process, and then deploy the resulting customized model directly through the Vertex AI platform for inference.

Yes, you need the google-cloud-aiplatform library to submit a Vertex AI tuning job. It is a required dependency for managing the environment setup, job submission, and deployment lifecycle on Google Cloud.