agent-platform-tuning

Name: agent-platform-tuning
Author: google

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

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. If the user names a model that is not in the catalog,
  follow the fallback rule in that catalog. Do not proceed with model
  configuration until the category is confirmed.
- Yes → Proceed.
Environment Check: Has the environment (Auth, APIs, IAM, Venv) been
initialized?
- No → Go to Phase 0: Environment & IAM Setup.
- Yes → Proceed.
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.
```
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.
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.

Job Status: Has the tuning job been submitted?

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

Job Completion: Is the tuning job complete?

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

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 is known. Use gcloud config get project to retrieve the
current project.
CRITICAL: Ask for Confirmation. You must prompt the user to confirm the
retrieved project before proceeding, in case they want to switch to a
different one. The location must also be confirmed — see section 0.2 for
which location to propose, which depends on the model category.

0.2 Location

Location handling depends on the model category you established in the
workflow decision tree. The two categories have different supported locations —
never apply one category's locations to the other.

Open models share one fixed location set, and global is the
recommended choice.
Gemini models differ per model and must be looked up. global is not
accepted for them today.

If the user names a location that is not valid for their model and category,
STOP. Respond with an error naming the requested location as unsupported, list
the locations that are valid, and do NOT ask for a dataset, do NOT proceed with
any other setup step, and do NOT silently retry elsewhere.

Open Models (RECOMMEND: `global`)

Recommend global and confirm it with the user. Propose it as a single
recommended choice rather than making the user pick a region first, and do not
steer them toward a specific region instead.

These are the only locations available for open model tuning:

global (the recommended choice)
us-central1
europe-west4
us-west1
us-east5
asia-southeast1

The global endpoint automatically selects a supported region that has
available capacity, so it is the most likely to be scheduled successfully.
Pinning a region up front restricts the job to that one region's capacity, which
is why global is the recommended location for open model tuning.

The user named a location → use it verbatim, provided it is global or
one of the regions listed above. Do not talk them out of it.
The user asked which locations are supported → answer the question.
Share the list above and say that global is recommended and why. Never
withhold it.
The user did not name a location → propose global and ask them to
confirm it before you proceed. Say that global lets the service pick a
region with available capacity. Do NOT silently assume global.

The point of proposing a single choice is to avoid making region selection a
decision the user must resolve before anything else can happen — that ordering
is what previously blocked people. It is not a reason to hide the list: quote it
whenever the user asks, and quote it when rejecting an unsupported location.

Fall back to an explicit region only in the cases below, and tell the user
why you are doing so:

CMEK. Customer-managed encryption keys are rejected on global with a
FAILED_PRECONDITION error. A CMEK-protected job must name the region that
holds the key.
Data residency. If the user requires the job to stay in a specific
jurisdiction, honor their region. global currently runs the job in either
us-central1 or europe-west4.

If a global job is accepted but then fails with a FAILED_PRECONDITION error
saying the model does not support global endpoint tuning, that model is not
onboarded to the global endpoint yet. The model itself is still tunable:
resubmit once in an explicit region from the list above (us-central1 is the
safest choice) and tell the user why you switched.

Working with a `global` job

The API host stays aiplatform.googleapis.com. There is no
global-aiplatform.googleapis.com host.
The service resolves global to a real region at run time. Sub-resources
(the tuned model, checkpoints, TensorBoard) come back with that real
region in their resource names, not global. Read the location out of the
returned resource name before using it for monitoring or deployment; never
assume it is still global.
Quota is shared across regions, so pinning a region does not grant extra
quota.

Gemini Models (per-model, look it up)

global is not accepted for Gemini tuning today — the service rejects it at
job creation with a FAILED_PRECONDITION error, so do not propose it here.

There is no single region allowlist for Gemini. Supported tuning regions
vary by model and by model version: some Gemini models are restricted to two
regions while others support many more. Do NOT reuse the open model list above,
and do NOT assume a region carries over from another Gemini model.

Before submitting, look up the chosen model in the supervised fine-tuning
documentation and read its "Supported endpoint for model tuning"
row:
supervised tuning

The user asked which regions are supported → look up that specific model
and tell them what the docs say. Do not answer from memory or from the open
model list, and do not answer for a different Gemini model.
The model's row names specific regions → the user's region must be one
of them. If it is not, STOP and report the supported regions for that model.
The model's row is absent or the docs are unclear → ask the user for the
region rather than guessing one.

Confirm the region with the user before proceeding. Note that some Gemini models
also restrict CMEK and serve tuned models only on the us and eu multi-region
endpoints, so check the same table for those limits before promising them.

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:
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 Python Dependencies

The scripts in this skill import vertexai (from google-cloud-aiplatform),
google-genai, google-cloud-storage, and datasets.

CRITICAL AGENT INSTRUCTION: Do not create a virtual environment, and do
not install anything before checking. A venv starts empty and hides packages the
environment already provides, forcing a redundant several-minute install.

Probe first and install only if the probe fails:

python3 -c "import vertexai, google.genai, google.cloud.storage, datasets" \
  || pip install -r references/requirements.txt

Then run every script with a plain python3 scripts/... — no activation prefix.

The references/requirements.txt pins are a fallback for an environment that
does not already provide these SDKs. Do not apply them on top of a working
environment: they would downgrade packages other tools may share.

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
prompt (or user > message) and completion (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.

ARTIFACTS="gs://YOUR_BUCKET/tuning_agent_job_<datetime>/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.

Verify the Live Model ID

Before submitting the job, run scripts/list_models.py and pick --base_model
only from its models output. Do not invent IDs or version numbers.

python3 scripts/list_models.py --project YOUR_PROJECT --filter gemini

Output: {"models": [...], "total_count": N, "truncated": bool}.

For Gemini, strip google/ and @default (e.g.
google/gemini-2.5-flash@default → gemini-2.5-flash); for open models,
pass publisher/family@version as-is.
Skip Gemini variants ending in -embedding, -tts, -image,
-computer-use, or -native-audio; they are not tunable.
If truncated is true, re-run with a tighter --filter (e.g.
gemini-2.5) before deciding the target version is unavailable.
If models is empty, stop and ask the user.

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 global \
    --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

This script is open model only, and --location falls back to global if
omitted. Always pass the location the user confirmed in section 0.2 explicitly,
so it is visible in the command string you present for approval.

[!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.
--location is required and must be the same location you submitted with: an
open model job submitted on global is polled with --location global, even
though the work runs in a real region behind the scenes.

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.

Deployment requires a real region — --region=global is not valid here. If the
job ran on global, read the region out of the tuned model's resource name
(projects/.../locations/<REGION>/models/...) and deploy there; do not guess.

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.

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