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Tune Translation LLM models by using supervised fine-tuning

This document describes how to tune a Translation LLM model by using supervised fine-tuning.

Before you begin

Before you begin, you must prepare a supervised fine-tuning dataset. Depending on your use case, there are different requirements.

Prepare a text dataset for tuning: Text tuning

Supported Models

translation-llm-002 (In preview, only supports text tuning)

Create a tuning job

You can create a supervised fine-tuning job by using the REST API or the Vertex AI SDK for Python.

REST

To create a model tuning job, send a POST request by using the tuningJobs.create method. Some of the parameters are not supported by all of the models. Ensure that you include only the applicable parameters for the model that you're tuning.

Before using any of the request data, make the following replacements:

PROJECT_ID : Your project ID .
TUNING_JOB_REGION : The region where the tuning job runs. This is also the default region for where the tuned model is uploaded. Supported region: us-central1 .
BASE_MODEL : Name of the translation model to tune. Supported values: translation-llm-002 .
TRAINING_DATASET_URI : Cloud Storage URI of your training dataset. The dataset must be formatted as a JSONL file. For best results, provide at least 100 to 500 examples. For more information, see About supervised tuning dataset .
VALIDATION_DATASET_URI Optional: The Cloud Storage URI of your validation dataset file.
TUNED_MODEL_DISPLAYNAME Optional: A display name for the tuned model. If not set, a random name is generated.

HTTP method and URL:

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

Request JSON body:

{
  "baseModel": " BASE_MODEL 
",
  "supervisedTuningSpec" : {
      "trainingDatasetUri": " TRAINING_DATASET_URI 
",
      "validationDatasetUri": " VALIDATION_DATASET_URI 
",
  },
  "tunedModelDisplayName": " TUNED_MODEL_DISPLAYNAME 
"
}

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell , which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list .

Save the request body in a file named request.json , and execute the following command:

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"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list .

Save the request body in a file named request.json , and execute the following command:

$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

You should receive a JSON response similar to the following.

Response

{
  "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 
",
    },
  "tunedModelDisplayName": " TUNED_MODEL_DISPLAYNAME 
"
}

Example curl command

  PROJECT_ID 
 = 
myproject LOCATION 
 = 
us-central1
curl  
 \ 
-X  
POST  
 \ 
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
-H  
 "Content-Type: application/json; charset=utf-8" 
  
 \ 
 "https:// 
 ${ 
 LOCATION 
 } 
 -aiplatform.googleapis.com/v1/projects/ 
 ${ 
 PROJECT_ID 
 } 
 /locations/ 
 ${ 
 LOCATION 
 } 
 /tuningJobs" 
  
 \ 
-d  
 \ 
 $'{ 
 "baseModel": "translation-llm-002", 
 "supervisedTuningSpec" : { 
 "training_dataset_uri": "gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl", 
 "validation_dataset_uri": "gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_validation_data.jsonl" 
 }, 
 "tunedModelDisplayName": "tuned_translation_llm" 
 }'

Python

  from 
  
 vertexai.generative_models 
  
 import 
  GenerativeModel 
 
 sft_tuning_job 
 = 
  sft 
 
 . 
  SupervisedTuningJob 
 
 ( 
 "projects/<PROJECT_ID>/locations/<TUNING_JOB_REGION>/tuningJobs/<TUNING_JOB_ID>" 
 ) 
 tuned_model 
 = 
 GenerativeModel 
 ( 
 sft_tuning_job 
 . 
 tuned_model_endpoint_name 
 ) 
 print 
 ( 
 tuned_model 
 . 
  generate_content 
 
 ( 
 content 
 )) 
 import 
  
 time 
 import 
  
  vertexai 
 
 from 
  
 vertexai.tuning 
  
 import 
  sft 
 
 # TODO(developer): Update and un-comment below line. 
 # PROJECT_ID = os.environ["GOOGLE_CLOUD_PROJECT"] 
  vertexai 
 
 . 
 init 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 "us-central1" 
 ) 
 sft_tuning_job 
 = 
  sft 
 
 . 
  train 
 
 ( 
 source_model 
 = 
 "translation-llm-002" 
 , 
 train_dataset 
 = 
 "gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl" 
 , 
 # The following parameters are optional 
 validation_dataset 
 = 
 "gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_validation_data.jsonl" 
 , 
 tuned_model_display_name 
 = 
 "tuned_translation_llm_002" 
 , 
 ) 
 # Polling for job completion 
 while 
 not 
 sft_tuning_job 
 . 
 has_ended 
 : 
 time 
 . 
 sleep 
 ( 
 60 
 ) 
 sft_tuning_job 
 . 
  refresh 
 
 () 
 print 
 ( 
 sft_tuning_job 
 . 
 tuned_model_name 
 ) 
 print 
 ( 
 sft_tuning_job 
 . 
 tuned_model_endpoint_name 
 ) 
 print 
 ( 
 sft_tuning_job 
 . 
 experiment 
 ) 
 # Example response: 
 # projects/123456789012/locations/us-central1/models/1234567890@1 
 # projects/123456789012/locations/us-central1/endpoints/123456789012345 
 # <google.cloud.aiplatform.metadata.experiment_resources.Experiment object at 0x7b5b4ae07af0>

View a list of tuning jobs

You can view a list of tuning jobs in your current project by using the Google Cloud console, the Vertex AI SDK for Python, or by sending a GET request by using the tuningJobs method.

REST

To view a list of model tuning jobs, send a GET request by using the tuningJobs.list method.

Before using any of the request data, make the following replacements:

PROJECT_ID : Your project ID .
TUNING_JOB_REGION : The region where the tuning job runs. This is also the default region for where the tuned model is uploaded.

HTTP method and URL:

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

To send your request, choose one of these options:

curl

Execute the following command:

curl -X GET \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
"https:// TUNING_JOB_REGION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ TUNING_JOB_REGION 
/tuningJobs"

PowerShell

Execute the following command:

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

Invoke-WebRequest `
-Method GET `
-Headers $headers `
-Uri "https:// TUNING_JOB_REGION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ TUNING_JOB_REGION 
/tuningJobs" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Response

{
  "tuning_jobs": [ TUNING_JOB_1 
, TUNING_JOB_2 
, ...
  ]
}

Python

  import 
  
 vertexai 
 from 
  
 vertexai.tuning 
  
 import 
 sft 
 # TODO(developer): Update and un-comment below line 
 # PROJECT_ID = "your-project-id" 
 vertexai 
 . 
 init 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 "us-central1" 
 ) 
 responses 
 = 
 sft 
 . 
 SupervisedTuningJob 
 . 
 list 
 () 
 for 
 response 
 in 
 responses 
 : 
 print 
 ( 
 response 
 ) 
 # Example response: 
 # <vertexai.tuning._supervised_tuning.SupervisedTuningJob object at 0x7c85287b2680> 
 # resource name: projects/12345678/locations/us-central1/tuningJobs/123456789012345

Console

To view your tuning jobs in the Google Cloud console, go to the Vertex AI Studiopage.

Go to Vertex AI Studio

Your Translation LLM tuning jobs are listed in the table under the Translation LLM tuned modelssection.

Get details of a tuning job

You can get the details of a tuning job in your current project by using the Google Cloud console, the Vertex AI SDK for Python, or by sending a GET request by using the tuningJobs method.

REST

To view a list of model tuning jobs, send a GET request by using the tuningJobs.get method and specify the TuningJob_ID .

Before using any of the request data, make the following replacements:

PROJECT_ID : .
TUNING_JOB_REGION : The region where the tuning job runs. This is also the default region for where the tuned model is uploaded.
TUNING_JOB_ID : The ID of the tuning job.

HTTP method and URL:

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

To send your request, choose one of these options:

curl

Execute the following command:

curl -X GET \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
"https:// TUNING_JOB_REGION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ TUNING_JOB_REGION 
/tuningJobs/ TUNING_JOB_ID 
"

PowerShell

Execute the following command:

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

Invoke-WebRequest `
-Method GET `
-Headers $headers `
-Uri "https:// TUNING_JOB_REGION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ TUNING_JOB_REGION 
/tuningJobs/ TUNING_JOB_ID 
" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Response

{
  "name": "projects/ PROJECT_ID 
/locations/ TUNING_JOB_REGION 
/tuningJobs/ TUNING_JOB_ID 
",
  "tunedModelDisplayName": " TUNED_MODEL_DISPLAYNAME 
",
  "createTime": CREATE_TIME 
,
  "endTime": END_TIME 
,
  "tunedModel": {
      "model": "projects/ PROJECT_ID 
/locations/ TUNING_JOB_REGION 
/models/ MODEL_ID 
",
      "endpoint": "projects/ PROJECT_ID 
/locations/ TUNING_JOB_REGION 
/endpoints/ ENDPOINT_ID 
"
  },
  "experiment": "projects/ PROJECT_ID 
/locations/ TUNING_JOB_REGION 
/metadataStores/default/contexts/ EXPERIMENT_ID 
",
  "tuning_data_statistics": {
      "supervisedTuningDataStats": {
          "tuninDatasetExampleCount": " TUNING_DATASET_EXAMPLE_COUNT 
",
          "totalBillableTokenCount": " TOTAL_BILLABLE_TOKEN_COUNT 
",
          "tuningStepCount": " TUNING_STEP_COUNT 
"
      }
  },
  "status": " STATUS 
",
  "supervisedTuningSpec" : {
        "trainingDatasetUri": " TRAINING_DATASET_URI 
",
        "validationDataset_uri": " VALIDATION_DATASET_URI 
",
        "hyperParameters": {
            "epochCount": EPOCH_COUNT 
,
            "learningRateMultiplier": LEARNING_RATE_MULTIPLIER 
}
    }
}

Python

  import 
  
 vertexai 
 from 
  
 vertexai.tuning 
  
 import 
 sft 
 # TODO(developer): Update and un-comment below lines 
 # PROJECT_ID = "your-project-id" 
 # LOCATION = "us-central1" 
 vertexai 
 . 
 init 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 LOCATION 
 ) 
 tuning_job_id 
 = 
 "4982013113894174720" 
 response 
 = 
 sft 
 . 
 SupervisedTuningJob 
 ( 
 f 
 "projects/ 
 { 
 PROJECT_ID 
 } 
 /locations/ 
 { 
 LOCATION 
 } 
 /tuningJobs/ 
 { 
 tuning_job_id 
 } 
 " 
 ) 
 print 
 ( 
 response 
 ) 
 # Example response: 
 # <vertexai.tuning._supervised_tuning.SupervisedTuningJob object at 0x7cc4bb20baf0> 
 # resource name: projects/1234567890/locations/us-central1/tuningJobs/4982013113894174720

Console

To view details of a tuned model in the Google Cloud console, go to the Vertex AI Studiopage.

Go to Vertex AI Studio
In the Translation LLM tuned modelstable, find your model and click Details.

The details of your model are shown.

Cancel a tuning job

You can cancel a tuning job in your current project by using the Google Cloud console, the Vertex AI SDK for Python, or by sending a POST request using the tuningJobs method.

REST

To view a list of model tuning jobs, send a GET request by using the tuningJobs.cancel method and specify the TuningJob_ID .

Before using any of the request data, make the following replacements:

PROJECT_ID : .
TUNING_JOB_REGION : The region where the tuning job runs. This is also the default region for where the tuned model is uploaded.
TUNING_JOB_ID : The ID of the tuning job.

HTTP method and URL:

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

To send your request, choose one of these options:

curl

Execute the following command:

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

PowerShell

Execute the following command:

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

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-Uri "https:// TUNING_JOB_REGION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ TUNING_JOB_REGION 
/tuningJobs/ TUNING_JOB_ID 
:cancel" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Response

{}

Python

  import 
  
 vertexai 
 from 
  
 vertexai.tuning 
  
 import 
 sft 
 # TODO(developer): Update and un-comment below lines 
 # PROJECT_ID = "your-project-id" 
 # LOCATION = "us-central1" 
 vertexai 
 . 
 init 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 LOCATION 
 ) 
 tuning_job_id 
 = 
 "4982013113894174720" 
 job 
 = 
 sft 
 . 
 SupervisedTuningJob 
 ( 
 f 
 "projects/ 
 { 
 PROJECT_ID 
 } 
 /locations/ 
 { 
 LOCATION 
 } 
 /tuningJobs/ 
 { 
 tuning_job_id 
 } 
 " 
 ) 
 job 
 . 
 cancel 
 ()

Console

To cancel a tuning job in the Google Cloud console, go to the Vertex AI Studiopage.

Go to Vertex AI Studio
In the Translation tuned modelstable, click Manage run.
Click Cancel.

Test the tuned model with a prompt

You can test a tuning job in your current project by using the Vertex AI SDK for Python or by sending a POST request using the tuningJobs method.

The following example prompts a model with the question "Why is sky blue?".

REST

To test a tuned model with a prompt, send a POST request and specify the TUNED_ENDPOINT_ID .

Before using any of the request data, make the following replacements:

PROJECT_ID : .
TUNING_JOB_REGION : The region where the tuning job runs. This is also the default region for where the tuned model is uploaded.
ENDPOINT_ID : The tuned model endpoint ID from the GET API.

HTTP method and URL:

POST https:// TUNING_JOB_REGION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ TUNING_JOB_REGION 
/endpoints/ ENDPOINT_ID 
:generateContent

Request JSON body:

{
    "contents": [
        {
            "role": "USER",
            "parts": {
                "text" : "English: Hello. Spanish:"
            }
        }
    ],
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json , and execute the following command:

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 
/endpoints/ ENDPOINT_ID 
:generateContent"

PowerShell

Save the request body in a file named request.json , and execute the following command:

$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 
/endpoints/ ENDPOINT_ID 
:generateContent" | Select-Object -Expand Content

You should receive a JSON response similar to the following.

Response

{
  "candidates": [
    {
      "content": {
        "role": "model",
        "parts": [English: Hello. Spanish:
          {
            "text": "Hola."
          }
        ]
      },
      "finishReason": "STOP",
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 5,
    "candidatesTokenCount": 33,
    "totalTokenCount": 38
  }
}

Python

  from 
  
 vertexai.generative_models 
  
 import 
 GenerativeModel 
 sft_tuning_job 
 = 
 sft 
 . 
 SupervisedTuningJob 
 ( 
 "projects/<PROJECT_ID>/locations/<TUNING_JOB_REGION>/tuningJobs/<TUNING_JOB_ID>" 
 ) 
 tuned_model 
 = 
 GenerativeModel 
 ( 
 sft_tuning_job 
 . 
 tuned_model_endpoint_name 
 ) 
 print 
 ( 
 tuned_model 
 . 
 generate_content 
 ( 
 content 
 ))

Tuning and validation metrics

You can configure a model tuning job to collect and report model tuning and model evaluation metrics, which can then be visualized in Vertex AI Studio.

To view details of a tuned model in the Google Cloud console, go to the Vertex AI Studiopage.

Go to Vertex AI Studio
In the Tune and Distilltable, click the name of the tuned model that you want to view metrics for.

The tuning metrics appear under the Monitortab.

Model tuning metrics

The model tuning job automatically collects the following tuning metrics for translation-llm-002 .

/train_total_loss : Loss for the tuning dataset at a training step.
/train_fraction_of_correct_next_step_preds : The token accuracy at a training step. A single prediction consists of a sequence of tokens. This metric measures the accuracy of the predicted tokens when compared to the ground truth in the tuning dataset.
/train_num_predictions: Number of predicted tokens at a training step.

Model validation metrics:

You can configure a model tuning job to collect the following validation metrics for translation-llm-002 .

/eval_total_loss : Loss for the validation dataset at a validation step.
/eval_fraction_of_correct_next_step_preds : The token accuracy at an validation step. A single prediction consists of a sequence of tokens. This metric measures the accuracy of the predicted tokens when compared to the ground truth in the validation dataset.
/eval_num_predictions : Number of predicted tokens at a validation step.

The metrics visualizations are available after the tuning job starts running. It will be updated in real time as tuning progresses. If you don't specify a validation dataset when you create the tuning job, only the visualizations for the tuning metrics are available.

What's next

To learn how supervised fine-tuning can be used in a solution that builds a generative AI knowledge base, see Jump Start Solution: Generative AI knowledge base .

Tune Translation LLM models by using supervised fine-tuning Stay organized with collections Save and categorize content based on your preferences.

Before you begin

Supported Models

Create a tuning job

REST

curl

PowerShell

Response

Example curl command

Python

View a list of tuning jobs

REST

curl

PowerShell

Response

Python

Console

Get details of a tuning job

REST

curl

PowerShell

Response

Python

Console

Cancel a tuning job

REST

curl

PowerShell

Response

Python

Console

Test the tuned model with a prompt

REST

curl

PowerShell

Response

Python

Tuning and validation metrics

Model tuning metrics

Model validation metrics:

What's next

Tune Translation LLM models by using supervised fine-tuning