agent-platform-tuning

name	agent-platform-tuning
description	>-

Trigger: Use when managing Tuning on Google Cloud's Agent Platform — Google Cloud AI and agent infrastructure.

Agent Platform Model Tuning

Overview

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

Workflow Decision Tree

1. Model Category Identification: Has the user explicitly stated whether they want to tune an Open Model or a Gemini Model?

   • No → STOP. Ask the user if they want to tune an Open Model or a Gemini Model. CRITICAL EXCEPTION for Environment Setup Requests: If the user is specifically asking for environment setup instructions (e.g. "What environment setup is needed?"), you MUST provide the full Phase 0 environment setup instructions in your initial response, simultaneously with asking clarifying questions about the model category.
   • If the user provides a specific tuning purpose, you should recommend three models: one Open Model, one Gemini Model, and a third generally recommended choice. Briefly list the pros and cons of each (e.g., Gemini models might be more expensive, etc.). CRITICAL: You must read references/models.md during this step and only recommend models explicitly listed in that catalog. Do not recommend unsupported models like Mistral. Do not proceed with model configuration until the category is confirmed.
   • Yes → Proceed.

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

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

3. Dataset Status: Is the dataset ready in JSONL format, is its structure valid for tuning, and is it uploaded to Google Cloud Storage?

   -   **No** → Go to [Phase 1: Dataset Preparation & Upload](#phase-1).
   -   **Yes** → Proceed.
   

4. Column Selection Confirmation: Have you presented the columns to the user and confirmed the mapping?

   • No → STOP. You must show samples and get user confirmation on column mapping as described in Phase 1.0 before proceeding.
   • Yes → Proceed.

5. Configuration: Has the user provided the target model and hyperparameters, or explicitly agreed to your recommendations?

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

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

   -   **No** → Go to
       [Phase 3: Tuning Job Execution](#phase-3-tuning-job-execution).
   -   **Yes** → Proceed.
   

7. Job Completion: Is the tuning job complete?

   -   **No** → Go to [Phase 4: Monitoring](#phase-4-monitoring).
   -   **Yes** → Proceed.
   

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

   -   **No** → Go to [Phase 5: Model Deployment](#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. If it is installed, update it:

gcloud components update --quiet > /dev/null 2>&1

• 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.

• Agent Platform Service Agent: [e-mail]
• Managed OSS Fine Tuning Service Agent: [e-mail]
• 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

CRITICAL AGENT INSTRUCTION: You MUST ensure that every Python command or script execution (e.g., python3 scripts/..., pip install ...) is prefixed with the virtual environment activation command: source ~/tuning_agent_venv/bin/activate &&. Additionally, advise the user that every single time they run a Python command, execute a script, or inspect data inline, they MUST also activate this virtual environment first in their bash execution. For example: source ~/tuning_agent_venv/bin/activate && python3 .... Do not run standalone python3 commands without activating the environment, as they will encounter ModuleNotFoundError issues.

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

1.0 Dataset Discovery & Confirmation

• User-Provided Dataset Verification: If the user specifies a dataset filename or path in their prompt, verify its existence in the workspace (e.g. via script execution or checking for typos).
  • If the file cannot be found anywhere, you MUST inform the user that the dataset file does not exist or cannot be accessed. You MUST prompt the user to provide a valid dataset path. Alternatively, if candidate dataset files are found in the workspace during your search, you MUST present the candidates to the user and ask them to select one. You MUST stop tool execution immediately after reporting the missing file or presenting candidates, and wait for the user's response. Do NOT ask for 80/20 validation split permission, and do NOT attempt to upload the dataset before receiving a valid dataset file selection from the user.
  • If the file is found and verified, proceed to Step 1.1 Formatting & Validation below.
• Auto-Discovery: From User Bucket: If the user does not have a dataset and no suitable alternative is found in the Hugging Face reference, offer to search the user's GCS buckets for potential training data. Prioritize searching for files with extensions like .jsonl, .json, .csv, and .parquet. If such files are found, read the first few lines/records of each to determine if they contain text-based data suitable for tuning (e.g., prompt/completion pairs) that can be modified to follow Data Preparation Guide and is related to the tuning task requested. DO NOT search without prompting first.
• Auto-Discovery: From Task to Huggingface: If the user has a specific task, refer to Huggingface Datasets Reference and recommend a dataset from this if one exists. For each dataset recommended, provide some information about the dataset and provide some reasonable splits. > [!IMPORTANT] > CRITICAL: Ask for Confirmation and Column Selection. Do not proceed > with dataset preparation or upload until you perform the following > steps and get user confirmation: > 1. Dataset and Split Confirmation: Present the dataset and > available splits to the user and have them confirm which to use. > 2. Column Selection (Hugging Face or Custom Datasets): You must: > - Provide a list of all available columns in the selected dataset > split. > - Show a few samples from the dataset to help the user > understand the content and make the choice of columns. > - Recommend which columns should be mapped to (or user > message) and (or assistant response), offering a few > reasonable options if applicable. > - Ask the user to confirm the column mapping or specify which > columns to use.

1.1 Formatting & Validation

• Conversion: If data is in CSV, JSON, or Parquet, 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. Simply having a .jsonl extension is not enough. You must verify that the content schema is valid for tuning (e.g. correct system/user/model roles).

python3 scripts/prepare_dataset.py \
    --input my_data.jsonl \
    --format <messages|messages_gemini> \
    --validate_only

(Use --format messages for open models and --format messages_gemini for Gemini models.) - 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.

`bash

ARTIFACTS="gs://YOUR_BUCKET/tuning_agent_job_/dataset.jsonl" gcloud storage cp dataset.jsonl $ARTIFACTS`

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

Help the user choose the best 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

For Open Models

• 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 (Open Models Only)

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

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

[!NOTE] Handling Missing Dataset Errors: If scripts/calculate_cost.py fails because the dataset file (e.g. my_data.jsonl or dummy_data.jsonl) cannot be found, you MUST inform the user that the dataset file does not exist or cannot be accessed. You MUST prompt the user to provide a valid dataset path, and stop tool execution immediately to wait for their response. Do NOT retry or loop, do NOT invent a specific cost number, and do NOT prompt for job submission approval before receiving a valid dataset from the user.

• 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}

CRITICAL Pre-Flight Check (GCS Verification): Before you propose a confirmation prompt or submit any tuning job, you MUST verify that the specified training dataset GCS URI (e.g. gs://dummy_bucket/dataset.jsonl or gs://YOUR_BUCKET/...) actually exists and is accessible. Run gcloud storage ls $DATASET_URI (or gsutil ls).

• If the verification fails (e.g. BucketNotFound, 404, AccessDenied, or indicating a dummy/missing bucket), you MUST inform the user that the GCS bucket or dataset does not exist or cannot be accessed. You MUST prompt the user to provide a valid GCS URI for the dataset, and stop tool execution immediately to wait for their response. Do NOT propose a confirmation prompt and do NOT execute any tuning scripts before receiving a valid dataset URI from the user.
• If the verification succeeds, proceed to propose the confirmation prompt below.

For Gemini Models

Check if scripts/tune_gemini_model.py exists.

• If scripts/tune_gemini_model.py exists: Submit the Gemini model tuning job using this script.

  python3 scripts/tune_gemini_model.py
  

• If scripts/tune_gemini_model.py does not exist: Instruct the user to manually configure and submit the tuning job via the Google Cloud Console UI or using the Agent Platform SDK for Python.

For Open Models

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 \
    --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

[!IMPORTANT] Interactive Confirmation Required (Tier M): Before proceeding with job submission, you MUST present the proposed command string showing all literal flags in a confirmation prompt to the user with 'Yes' and 'No' options.

CRITICAL: When presenting this confirmation prompt to the user, you MUST output it as a direct plain text response and stop tool execution immediately. Do NOT call any command execution or interactive tools in the same turn, as unexpected tool calls may be auto-replied by the simulation harness and cause an infinite loop. Yield immediately for the user's reply.

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

Monitor the job via the Cloud Console link provided in the script output. Additionally, ask the user if they want you to monitor the job status for them in the background. If they agree, execute scripts/monitor_tuning_job.py as a background task to periodically poll the job status and notify the user to show the status. If the user declines, leave it completely to the user to check on the status.

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

Once the tuning job is SUCCEEDED, deploy the model.

ARTIFACTS="gs://YOUR_BUCKET/tuning_agent_job_<datetime>/output/postprocess/node-0/checkpoints/final"
gcloud ai model-garden models deploy \
    --project=YOUR_PROJECT \
    --region=YOUR_LOCATION \
    --model="$ARTIFACTS" \
    --machine-type=MACHINE_TYPE \
    --accelerator-type=ACCELERATOR_TYPE \
    --accelerator-count=COUNT

[!IMPORTANT] Interactive Confirmation Required (Tier M): Before proceeding with deployment, you MUST present the proposed command string showing all literal flags in a confirmation prompt to the user with 'Yes' and 'No' options.

CRITICAL: When presenting this confirmation prompt to the user, you MUST output it as a direct plain text response and stop tool execution immediately. Do NOT call any command execution or interactive tools in the same turn, as unexpected tool calls may be auto-replied by the simulation harness and cause an infinite loop. Yield immediately for the user's reply.

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.