name: vertex-tuning-open-model description: > Vertex AI Open Model Tuning. Use when you need to fine-tune open models using Vertex AI's infrastructure.

Vertex AI Open Model Tuning

Overview

This skill provides procedural knowledge for fine-tuning Open Large Language Models (LLMs) using Vertex AI's tuning service. It covers the entire lifecycle from environment setup and data preparation to job configuration, monitoring, and deployment.

Workflow Decision Tree

1. Environment Check: Has the environment (Auth, APIs, IAM, Venv) been initialized?

   • No → Go to Phase 0: Environment & IAM Setup.
   • Yes → Proceed.

2. Dataset Status: Is the dataset ready in JSONL format and uploaded to GCS?

   • No → Go to Phase 1: Dataset Preparation & Upload.
   • Yes → Proceed.

3. Configuration: Have the target open model and hyperparameters been decided?

   • No → Go to Phase 2: Model Configuration & Recommendation.
   • Yes → Proceed.

4. Job Status: Has the tuning job been submitted?

   • No → Go to Phase 3: Tuning Job Execution.
   • Yes → Proceed.

5. Job Completion: Is the tuning job complete?

   • No → Go to Phase 4: Monitoring.
   • Yes → Proceed.

6. Deployment: Has the tuned model been deployed (if required)?

   • No → Go to Phase 5: Model Deployment.
   • Yes → Task Complete.

Phase 0: Environment & IAM Setup {#phase-0}

Ensure the foundational environment is ready before proceeding.

0.1 Authentication & Project Context

• Check if gcloud CLI is installed. If it is not installed, prompt the user for permission to install it before proceeding.
• Verify gcloud auth list. If not authenticated, run gcloud auth login.
• Ensure project and location are known. Use gcloud config get project to retrieve the current project (and gcloud config get compute/region for region).
• CRITICAL: Ask for Confirmation. You must prompt the user to confirm the retrieved project and region before proceeding, in case they want to switch to a different one.

0.2 Possible Locations

The following locations are available for tuning:

• us-central1
• europe-west4
• us-west1
• us-east5
• asia-southeast1

No other values are supported for this section, ensure that the location is listed above.

0.3 Enable APIs

Ensure aiplatform.googleapis.com and storage.googleapis.com are enabled.

gcloud services enable aiplatform.googleapis.com storage.googleapis.com --project=YOUR_PROJECT

0.4 IAM Permissions

Verify the following identities have the required roles.

• Vertex AI Service Agent: service-PROJECT_NUMBER@gcp-sa-aiplatform.iam.gserviceaccount.com
• Managed OSS Fine Tuning Service Agent: service-PROJECT_NUMBER@gcp-sa-vertex-moss-ft.iam.gserviceaccount.com
• User Identity: The account running the commands.

0.5 Virtual Environment

Create and use a virtual environment named tuning_agent_venv in the home directory. Install dependencies from references/requirements.txt.

python3 -m venv ~/tuning_agent_venv
source ~/tuning_agent_venv/bin/activate
pip install -r references/requirements.txt

Phase 1: Dataset Preparation & Upload {#phase-1}

Vertex AI requires valid JSONL format in GCS.

1.0 Dataset Discovery & Confirmation

• Ask the User First: Ask the user if they already have a dataset they want to use.
• Auto-Discovery: If the user does not have a dataset, search the authenticated project's GCS buckets to find if any existing file has a reasonable dataset that can do the job the user prompted initially.
• CRITICAL: Ask for Confirmation. Do not proceed with dataset preparation or upload until you present the found or provided dataset to the user and they confirm the dataset to use.

1.1 Formatting & Validation

• Conversion: If data is in CSV or JSON, use scripts/prepare_dataset.py to convert.
• Validation Split Confirmation: If the user only provides a training dataset, you must prompt the user to seek permission to split the training dataset 80/20 to form a validation dataset (using --validation_split 0.2). If they agree, proceed with the split. If they decline, just use the training dataset without a validation dataset.
• Validation: If data is already in JSONL, validate it before uploading: bash python3 scripts/prepare_dataset.py \ --input my_data.jsonl \ --format messages \ --validate_only
• Refer to Data Preparation Guide for required schemas.

1.2 Upload

Upload formatted .jsonl files to GCS using a unique directory (e.g., with a datetime timestamp) to avoid overwriting outputs from different runs.

gcloud storage cp dataset.jsonl gs://YOUR_BUCKET/tuning_agent_job_<datetime>/dataset.jsonl

Phase 2: Model Configuration & Recommendation {#phase-2}

Help the user choose the best open model and parameters. Always seek user confirmation before submitting the job.

• If the user does not specify a specific model in their prompt, calculate recommendations based on the Models Catalog.
• Prompt for Confirmation: Present the recommended model to the user and ask for their confirmation before configuring hyperparameters.

2.1 Configuration

• Recommend tuning_mode, epochs, learning_rate, and adapter_size based on the Tuning Guide and model-specific baselines in the Models Catalog.

2.2 Calculating Cost

• We can calculate a rough estimate of cost of tuning based on the dataset and the selected model in the Models Catalog:

  python3 \
      open-model/scripts/calculate_cost.py \
      --input my_data.jsonl \
      --model MODEL_NAME \
      --tuning_mode TUNING_MODE \
      --epochs EPOCHS

• Prompt for Confirmation: Present the recommended hyperparameter configuration and estimated cost to the user and ask for their approval before proceeding to job submission. Make sure to note that the estimated cost is just an estimate and can vary from actual billing costs.

Phase 3: Tuning Job Execution {#phase-3-tuning-job-execution}

Submit the open model tuning job using scripts/tune_open_model.py. Identify the model id using available models documentation at documentation.

python3 scripts/tune_open_model.py \
    --project YOUR_PROJECT \
    --location YOUR_LOCATION \
    --bucket YOUR_STAGING_BUCKET \
    --base_model BASE_MODEL_ID \
    --train_dataset gs://YOUR_BUCKET/tuning_agent_job_<datetime>/dataset.jsonl \
    --output_uri gs://YOUR_BUCKET/tuning_agent_job_<datetime>/output \
    --epochs EPOCHS \
    --learning_rate LR \
    --tuning_mode MODE

Phase 4: Monitoring {#phase-4-monitoring}

Monitor the job via the Cloud Console link provided in the script output or by polling the job status.

Phase 5: Model Deployment {#phase-5-model-deployment}

Once the open model tuning job is SUCCEEDED, deploy the model using scripts/deploy_open_model.py.

python3 scripts/deploy_open_model.py \
    --project YOUR_PROJECT \
    --location YOUR_LOCATION \
    --artifacts_uri gs://YOUR_BUCKET/tuning_agent_job_<datetime>/output/postprocess/node-0/checkpoints/final \
    --machine_type MACHINE_TYPE \
    --accelerator_type ACCELERATOR_TYPE \
    --accelerator_count COUNT

Refer to Models Catalog for hardware recommendations for specific open models.

Resources

• Data Preparation Guide
• Models Catalog
• Tuning Guide
• scripts/prepare_dataset.py: Data conversion & validation.
• scripts/tune_open_model.py: Open model tuning job submission.
• scripts/deploy_open_model.py: Open model deployment.