Tuning a text generation model

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Yandex Cloud

Improved by

Updated at April 22, 2025

Getting started
Upload the dataset
Start tuning
- Accessing a fine-tuned model

Model tuning based on the LoRA method is at the Preview stage.

This example shows how to fine-tune a YandexGPT Lite model based on the LoRA method in Foundation Models. Links to other examples are available in the See also section.

Getting started

To use the examples:

Management console

SDK

cURL

You can start working from the management console right away.

Create a service account and assign the ai.editor role to it.
Get the service account API key and save it.

The following examples use API key authentication. Yandex Cloud ML SDK also supports IAM token and OAuth token authentication. For more information, see Authentication in Yandex Cloud ML SDK.
Use the pip package manager to install the ML SDK library:
```
pip install yandex-cloud-ml-sdk
```

Get API authentication credentials as described in Authentication with the Yandex Foundation Models API.
Install gRPCurl.
To use the examples, install cURL.
(Optional) Install the jq JSON stream processor.
Get an IAM token used for authentication in the API.

Note

The IAM token has a short lifetime: no more than 12 hours.

Prepare UTF-8-encoded model tuning data in JSON Lines format. If you want to split your data into two datasets for tuning and validation, repeat the steps below for each dataset. Use the IDs you got after uploading the datasets to start the fine-tuning process.

In this example, we only use the tuning dataset for fine-tuning.

Create a tuning dataset:

Management console

SDK

cURL

In the management console, select the folder for which your account has the ai.playground.user and ai.datasets.editor roles or higher.
From the list of services, select Foundation Models.
In the left-hand panel, click Datasets.
Click Create dataset.
Enter a name and descriptions for the dataset. Follow these naming requirements:
- It must be from 2 to 63 characters long.
- It may contain lowercase Latin letters, numbers, and hyphens.
- It must start with a letter and cannot end with a hyphen.
In the Type field, select Text generation.
Delete or add dataset labels. You can use them to split or join resources into logical groups.
Click Select file or drag the JSON file you created earlier to the loading area.
Click Create dataset.

Create a file named dataset-create.py and add the following code to it:

#!/usr/bin/env python3

from __future__ import annotations

import pathlib

from yandex_cloud_ml_sdk import YCloudML
from yandex_cloud_ml_sdk.exceptions import DatasetValidationError


def main() -> None:

    sdk = YCloudML(
        folder_id="<folder_ID>",
        auth="<API_key>",
    )

    # Viewing the list of all previously uploaded datasets
    for dataset in sdk.datasets.list():
        print(f"List of existing datasets {dataset=}")

    # Deleting all previously uploaded datasets
    for dataset in sdk.datasets.list():
        dataset.delete()

    # Creating a tuning dataset for the YandexGPT Lite base model
    dataset_draft = sdk.datasets.draft_from_path(
        task_type="TextToTextGeneration",
        path="<file_path>",
        upload_format="jsonlines",
        name="YandexGPT Lite tuning",
    )

    # Waiting for the data to be uploaded and the dataset to be created
    operation = dataset_draft.upload_deferred()
    tuning_dataset = operation.wait()
    print(f"new {tuning_dataset=}")

if __name__ == "__main__":
    main()

Where:

<folder_ID>: ID of the folder the service account was created in.
<API_key>: Service account API key you got earlier required for authentication in the API.

The following examples use API key authentication. Yandex Cloud ML SDK also supports IAM token and OAuth token authentication. For more information, see Authentication in Yandex Cloud ML SDK.
<file_path>: Path to the file containing the ready-made examples for model tuning.

Run the created file:

python3 dataset-create.py

Result:

new tuning_dataset=Dataset(id='fdsv21bmp09v********', folder_id='b1gt6g8ht345********', name=
'YandexGPT Lite tuning', description=None, metadata=None, created_by='ajegtlf2q28a********', 
created_at=datetime.datetime(2025, 3, 12, 19, 31, 44), updated_at=datetime.datetime(2025, 3, 
12, 19, 32, 20), labels=None, allow_data_logging=False, status=<DatasetStatus.READY: 3>, 
task_type='TextToTextGeneration', rows=3, size_bytes=3514, validation_errors=())

Save the new dataset's ID (the id field value): you will need it when fine-tuning the model.

Create a dataset:

grpcurl \
  -H "Authorization: Bearer <IAM_token>" \
  -d @ \
  llm.api.cloud.yandex.net:443 yandex.cloud.ai.dataset.v1.DatasetService/Create <<EOM
  {
    "folder_id": "<folder_ID>", 
    "name": "My awesome dataset", 
    "task_type": "TextToTextGeneration", 
    "upload_format": "jsonlines"
  }
EOM

Where:

<IAM_token>: IAM token of the service account you got before you started.
<folder_ID>: ID of the folder you are creating the dataset in.

Result:

{
  "datasetId": "fdso08c1u1cq********",
  "dataset": {
    "datasetId": "fdso08c1u1cq********",
    "folderId": "b1gt6g8ht345********",
    "name": "My awesome dataset",
    "status": "DRAFT",
    "taskType": "TextToTextGeneration",
    "createdAt": "2025-01-20T10:36:50Z",
    "updatedAt": "2025-01-20T10:36:50Z",
    "createdById": "ajeg2b2et02f********",
    "createdBy": "ajeg2b2et02f********"
  }
}

Save the new dataset's ID (the datasetId field value): you will need it to upload data to the dataset.

Get a link to upload data into the dataset:

grpcurl \
  -H "Authorization: Bearer <IAM_token>" \
  -d '{"dataset_id": "<dataset_ID>", "size_bytes": <dataset_size>}' \
  llm.api.cloud.yandex.net:443 yandex.cloud.ai.dataset.v1.DatasetService/GetUploadDraftUrl | jq

Where:

<IAM_token>: IAM token of the service account you got before you started.
<dataset_ID>: Dataset ID you saved in the previous step.
<dataset_size>: Size in bytes of the file with data for tuning. In the terminal, you can get the file size using the ls -l <file_path> command.

Result:

{
  "datasetId": "fdso08c1u1cq********",
  "uploadUrl": "https://storage.yandexcloud.net/ai-fomo-drafts-prod/b1gt6g8ht345********/fdso08c1u1cq********?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20250120T105352Z&X-Amz-SignedHeaders=content-length%3Bhost&X-Amz-Expires=86400&X-Amz-Credential=YCAJE_WuJJ9D1r6huCoc8I3yO%2F20250120%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=611d7951994ae939acf4d32cc0c154c738d02adb2a04707a704f34ca********"
}

The uploadUrl field of the response contains a link you can use to upload your data into the dataset.

Tip

If you did not use jq, replace all \u0026 occurrences with & in the link to use it to upload the dataset.

Upload your data by specifying the link you got in the previous step and the path to the fine-tuning data file:
```
curl \
  --request PUT \
  --upload-file <path_to_file> \
  "<link>"
```

After the data upload is complete, run the dataset validation:

grpcurl \
  -H "Authorization: Bearer <IAM_token>" \
  -d '{"dataset_id": "<dataset_ID>"}' \
  llm.api.cloud.yandex.net:443 yandex.cloud.ai.dataset.v1.DatasetService/Validate

Where:

<IAM_token>: IAM token of the service account you got before you started.
<dataset_ID>: Dataset ID you saved in the previous step.

Result:

{
  "id": "fdso01v2jdd4********",
  "createdAt": "2025-01-20T11:03:48Z",
  "modifiedAt": "2025-01-20T11:03:48Z"
}

Save the validation operation ID (id field). You will need it in the next step.

Dataset validation may take some time. To find out validation status and get an error report (if any), send this request:

grpcurl \
  -H "Authorization: Bearer <IAM_token>" \
  -d '{"operation_id": "<validation_operation_ID>"}' \
  llm.api.cloud.yandex.net:443 yandex.cloud.operation.OperationService/Get

Where:

<IAM_token>: IAM token of the service account you got before you started.
<validation_operation_ID>: ID of the validation operation you saved in the previous step.

Result:

{
  "id": "fdso01v2jdd4********",
  "createdAt": "2025-01-20T11:03:48Z",
  "modifiedAt": "2025-01-20T11:04:46Z",
  "done": true,
  "response": {
    "@type": "type.googleapis.com/yandex.cloud.ai.dataset.v1.ValidateDatasetResponse",
    "datasetId": "fdso08c1u1cq********",
    "isValid": true
  }
}

The isValid field is set to true. This means the loaded dataset was validated successfully.

Start tuning

Management console

SDK

cURL

In the management console, select the folder for which your account has the ai.playground.user, ai.datasets.user, and ai.models.editor roles or higher.
From the list of services, select Foundation Models.
In the left-hand panel, click Fine-tuning models.
Click Fine-tune model.
Enter a name and descriptions for the dataset. Follow these naming requirements:
- It must be from 2 to 63 characters long.
- It may contain lowercase Latin letters, numbers, and hyphens.
- It must start with a letter and cannot end with a hyphen.
In the Task field, select Generation.
Optionally, add or delete the tuning labels. You can use them to split or join resources into logical groups.
In the Model field, select the model you need.
In the Dataset field, click Add.
In the window that opens, go to the Select from existings tab and select the dataset you created earlier.
Click Advanced settings to do advanced fine-tuning setup.
Click Start fine-tuning.

Create a file named start-tuning.py and add the following code to it:

#!/usr/bin/env python3

from __future__ import annotations
import pathlib
import uuid
from yandex_cloud_ml_sdk import YCloudML


def main():
    sdk = YCloudML(
        folder_id="<folder_ID>",
        auth="<API_key>",
    )

    # Viewing the list of valid datasets
    for dataset in sdk.datasets.list(status="READY", name_pattern="completions"):
        print(f"List of existing datasets {dataset=}")

    # Setting the tuning dataset and the base model
    train_dataset = sdk.datasets.get("<dataset_ID>")
    base_model = sdk.models.completions("yandexgpt-lite")

    # Starting the tuning
    # Tuning can last up to several hours
    tuned_model = base_model.tune(
        train_dataset, name=str(uuid.uuid4()), n_samples=10000
    )
    print(f"Resulting {tuned_model}")

    # You can access the fine-tuned model using the run() method
    completion_result = tuned_model.run("Hello!")
    print(f"{completion_result=}")

    # Or you can save the URI of the fine-tuned model
    # And call the fine-tuned model by its URI
    tuned_uri = tuned_model.uri
    model = sdk.models.completions(tuned_uri)
    completion_result = model.run("How are you doing?")
    print(f"{completion_result=}")


if __name__ == "__main__":
    main()

Where:

<folder_ID>: ID of the folder the service account was created in.
<API_key>: Service account API key you got earlier required for authentication in the API.

The following examples use API key authentication. Yandex Cloud ML SDK also supports IAM token and OAuth token authentication. For more information, see Authentication in Yandex Cloud ML SDK.
<dataset_ID>: The new dataset's ID you saved in the previous step.

Run the created file:

python3 start-tuning.py

Result:

Resulting GPTModel(uri=gpt://b1gt6g8ht345********/yandexgpt-lite/latest@tamrmgia22979********, 
config=GPTModelConfig(temperature=None, max_tokens=None, reasoning_mode=None, response_format=None))
completion_result=GPTModelResult(alternatives=(Alternative(role='assistant', text='Hi! How's it going?', 
status=<AlternativeStatus.FINAL: 3>),), usage=Usage(input_text_tokens=12, completion_tokens=5, 
total_tokens=17), model_version='23.10.2024')
completion_result=GPTModelResult(alternatives=(Alternative(role='assistant', text='Hello! I'm 
fine, thank you. How are you doing?', status=<AlternativeStatus.FINAL: 3>),), usage=Usage
(input_text_tokens=13, completion_tokens=13, total_tokens=26), model_version='23.10.2024')

Model tuning may take up to 1 day depending on the size of the dataset and the system load.

Use the fine-tuned model's URI you got (the uri field value) when accessing the model.

Fine-tuning metrics are available in TensorBoard format. You can open the downloaded file, for example, in the Yandex DataSphere project:
```
metrics_url = new_model.get_metrics_url()
download_tensorboard(metrics_url)
```

Start tuning:

grpcurl \
  -H "Authorization: Bearer <IAM_token>" \
  -d @ \
  llm.api.cloud.yandex.net:443 yandex.cloud.ai.tuning.v1.TuningService/Tune <<EOM
  {
    "base_model_uri": "gpt://<folder_ID>/yandexgpt-lite/latest",
    "train_datasets": [{"dataset_id": "<dataset_ID>", "weight": 1.0}],
    "name": "my first model",
    "text_to_text_completion": {}
  } 
EOM

Where:

<IAM_token>: IAM token of the service account you got before you started.
<folder_ID>: ID of the folder you are fine-tuning the model in.
<dataset_ID>: Dataset ID you saved in the previous step.

Result:

{
  "id": "ftnlljf53kil********",
  "createdAt": "2025-01-20T11:17:33Z",
  "modifiedAt": "2025-01-20T11:17:33Z",
  "metadata": {
    "@type": "type.googleapis.com/yandex.cloud.ai.tuning.v1.TuningMetadata"
  }
}

You will get the Operation object in response. Save the operation id you get in the response.

Model tuning may take up to one day depending on the dataset size and the system load. To check if the fine-tuning is complete, request the operation status:

grpcurl \
  -H "Authorization: Bearer <IAM_token>" \
  -d '{"operation_id": "<operation_ID>"}' \
  llm.api.cloud.yandex.net:443 yandex.cloud.operation.OperationService/Get

Where:

<IAM_token>: IAM token of the service account you got before you started.
<operation_ID>: Model fine-tuning operation ID you got in the previous step.

If the fine-tuning process is over, the Operation object will contain the tuned model's URI in the targetModelUri field:

{
  "id": "ftnlljf53kil********",
  "createdAt": "2025-01-20T11:17:33Z",
  "modifiedAt": "2025-01-20T11:25:40Z",
  "done": true,
  "metadata": {
    "@type": "type.googleapis.com/yandex.cloud.ai.tuning.v1.TuningMetadata",
    "status": "COMPLETED",
    "tuningTaskId": "ftnlljf53kil********"
  },
  "response": {
    "@type": "type.googleapis.com/yandex.cloud.ai.tuning.v1.TuningResponse",
    "status": "COMPLETED",
    "targetModelUri": "gpt://b1gt6g8ht345********/yandexgpt-lite/latest@tamr2nc6pev5e********",
    "tuningTaskId": "ftnlljf53kil********"
  }
}

Use the fine-tuned model's URI you got (the targetModelUri field value) when accessing the model.

Fine-tuning metrics are available in TensorBoard format. Get the link to download the file:

grpcurl \
  -H "Authorization: Bearer <IAM_token>" \
  -d '{"task_id": "<job_ID>"}' \
  llm.api.cloud.yandex.net:443 yandex.cloud.ai.tuning.v1.TuningService/GetMetricsUrl

You can open the downloaded file, for example, in the Yandex DataSphere project:

Accessing a fine-tuned model

Once the model is fine-tuned, save its URI in gpt://<base_model_URI>/<version>@<tuning_suffix> format. Use it to send synchronous and asynchronous requests or create an AI assistant based on the fine-tuned model.

Tuning a text generation model

Getting startedGetting started

Upload the datasetUpload the dataset

Start tuningStart tuning

Accessing a fine-tuned modelAccessing a fine-tuned model

See alsoSee also

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Getting started

Upload the dataset

Start tuning

Accessing a fine-tuned model

See also