Continuous tuning lets you continue tuning an already tuned model ormodel checkpointby adding more epochs or training examples. Using an already tuned model orcheckpoint as a base model allows for more efficient tuning experimentation.

You can use continuous tuning for the following purposes:

• To tune with more data if an existing tuned model isunderfitting.
• To boost performance or keep the model up to date with new data.
• To further customize an existing tuned model.

The following Gemini models support continuous tuning:

• Gemini 2.5 Pro
• Gemini 2.5 Flash
• Gemini 2.5 Flash-Lite

For detailed information about Gemini model versions, seeGoogle models andModel versions and lifecycle.

Configure continuous tuning

When creating a continuous tuning job, note the following:

• Continuous tuning is supported in the Google Gen AI SDK. It isn'tsupported in the Vertex AI SDK for Python.

• You must provide a model resource name:

  • In the Google Cloud console, the model resource name appears in theVertex AI Tuning page, in theTuning details > Model Namefield.
  • The model resource name uses the following format:

  projects/{project}/locations/{location}/models/{modelId}@{version_id}

  • {version_id} is optional and can be either the generated version ID or auser-provided version alias. If it is omitted, the default version is used.

• If you don't specify a model version, the default version is used.

• If you're using a checkpoint as a base model and don't specify a checkpointID, the default checkpoint is used. For more information, seeUse checkpoints in supervised fine-tuning for Gemini models.In the Google Cloud console, the default checkpoint can be found as follows:

  1. Go to the Model Registry page.
  2. Click theModel Name for the model.
  3. ClickView all versions.
  4. Click the desired version to view a list of checkpoints. The defaultcheckpoint is indicated by the worddefault next to the checkpoint ID.

• By default, a new model version is created under the same parent model as thepre-tuned model. If you supply a new tuned model display name, a new model iscreated.

• Only supervised tuning base models that are tuned on or after July 11, 2025can be used as base models for continuous tuning.

• If you're usingcustomer-managed encryption keys (CMEK),your continuous tuning job must use the same CMEK that was used in the tuningjob for the pre-tuned model.

POST https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs

{  "preTunedModel": {      "tunedModelName": "TUNED_MODEL_NAME",      "checkpointId": "CHECKPOINT_ID",  },  "supervisedTuningSpec" : {      "trainingDatasetUri": "TRAINING_DATASET_URI",      "validationDatasetUri": "VALIDATION_DATASET_URI",      "hyperParameters": {          "epochCount":EPOCH_COUNT,          "adapterSize": "ADAPTER_SIZE",          "learningRateMultiplier": "LEARNING_RATE_MULTIPLIER"      },      "exportLastCheckpointOnly":EXPORT_LAST_CHECKPOINT_ONLY,      "evaluationConfig": {          "metrics": [              {                  "aggregation_metrics": ["AVERAGE", "STANDARD_DEVIATION"],                  "METRIC_SPEC": {                      "METRIC_SPEC_FIELD_NAME":METRIC_SPEC_FIELD_CONTENT                  }              },          ],          "outputConfig": {              "gcs_destination": {                  "output_uri_prefix": "CLOUD_STORAGE_BUCKET"              }          },      },  },  "tunedModelDisplayName": "TUNED_MODEL_DISPLAYNAME",  "encryptionSpec": {    "kmsKeyName": "KMS_KEY_NAME"  },  "serviceAccount": "SERVICE_ACCOUNT"}

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs"

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://TUNING_JOB_REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs" | Select-Object -Expand Content

{  "name": "projects/PROJECT_ID/locations/TUNING_JOB_REGION/tuningJobs/TUNING_JOB_ID",  "createTime":CREATE_TIME,  "updateTime":UPDATE_TIME,  "status": "STATUS",  "supervisedTuningSpec": {        "trainingDatasetUri": "TRAINING_DATASET_URI",        "validationDatasetUri": "VALIDATION_DATASET_URI",        "hyperParameters": {            "epochCount":EPOCH_COUNT,            "adapterSize": "ADAPTER_SIZE",            "learningRateMultiplier":LEARNING_RATE_MULTIPLIER        },    },  "tunedModelDisplayName": "TUNED_MODEL_DISPLAYNAME",  "encryptionSpec": {    "kmsKeyName": "KMS_KEY_NAME"  },  "serviceAccount": "SERVICE_ACCOUNT"}

PROJECT_ID=myprojectLOCATION=globalcurl\-XPOST\-H"Authorization: Bearer$(gcloudauthprint-access-token)"\-H"Content-Type: application/json; charset=utf-8"\"https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/tuningJobs"\-d\$'{   "preTunedModel": "gemini-2.5-flash",   "supervisedTuningSpec" : {      "trainingDatasetUri": "gs://cloud-samples-data/ai-platform/generative_ai/gemini/text/sft_train_data.jsonl",      "validationDatasetUri": "gs://cloud-samples-data/ai-platform/generative_ai/gemini/text/sft_validation_data.jsonl"   },   "tunedModelDisplayName": "tuned_gemini"}'

importtimefromgoogleimportgenaifromgoogle.genai.typesimportHttpOptions,TuningDataset,CreateTuningJobConfig# TODO(developer): Update and un-comment below line# tuned_model_name = "projects/123456789012/locations/us-central1/models/1234567890@1"# checkpoint_id = "1"client=genai.Client(http_options=HttpOptions(api_version="v1beta1"))training_dataset=TuningDataset(gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini/text/sft_train_data.jsonl",)validation_dataset=TuningDataset(gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini/text/sft_validation_data.jsonl",)tuning_job=client.tunings.tune(base_model=tuned_model_name,# Note: Using a Tuned Modeltraining_dataset=training_dataset,config=CreateTuningJobConfig(tuned_model_display_name="Example tuning job",validation_dataset=validation_dataset,pre_tuned_model_checkpoint_id=checkpoint_id,),)running_states=set(["JOB_STATE_PENDING","JOB_STATE_RUNNING",])whiletuning_job.stateinrunning_states:print(tuning_job.state)tuning_job=client.tunings.get(name=tuning_job.name)time.sleep(60)print(tuning_job.tuned_model.model)print(tuning_job.tuned_model.endpoint)print(tuning_job.experiment)# Example response:# projects/123456789012/locations/us-central1/models/1234567890@2# projects/123456789012/locations/us-central1/endpoints/123456789012345# projects/123456789012/locations/us-central1/metadataStores/default/contexts/tuning-experiment-2025010112345678iftuning_job.tuned_model.checkpoints:fori,checkpointinenumerate(tuning_job.tuned_model.checkpoints):print(f"Checkpoint{i+1}: ",checkpoint)# Example response:# Checkpoint 1:  checkpoint_id='1' epoch=1 step=10 endpoint='projects/123456789012/locations/us-central1/endpoints/123456789000000'# Checkpoint 2:  checkpoint_id='2' epoch=2 step=20 endpoint='projects/123456789012/locations/us-central1/endpoints/123456789012345'