client.trainings.updateMetadata()

import { createClient } from "@toloka-platform/sdk";

const client = createClient({
  baseUrl: "https://platform.toloka.ai",
  apiKey: process.env.TOLOKA_API_KEY!,
});

const training = await client.trainings.updateMetadata({
  trainingId: "trg.019a0000-0000-7000-8000-000000000001",
  name: "Text classifier v2"
});
console.log(training);

{
  "id": "trg.019a0000-0000-7000-8000-000000000001",
  "projectId": "pp.019a0000-0000-7000-8000-000000000001",
  "modelId": null,
  "name": "<string>",
  "configuration": {
    "method": "sft",
    "source": {
      "type": "base_model",
      "baseModelId": "qwen3-4b"
    },
    "loraRank": 8,
    "epochs": 1,
    "learningRate": 0.00001,
    "batchSize": 8,
    "maxSequenceLength": 64,
    "training": {
      "datasetId": "ds.019a0000-0000-7000-8000-000000000001",
      "itemFilter": {
        "fieldValues": [
          {}
        ],
        "fieldSources": [
          {}
        ],
        "missingRevisionFields": [
          "df.019a0000-0000-7000-8000-000000000001"
        ]
      }
    }
  },
  "archivedAt": null,
  "createdAt": "2026-01-01T00:00:00Z",
  "updatedAt": "2026-01-01T00:00:00Z"
}

import { createClient } from "@toloka-platform/sdk";

const client = createClient({
  baseUrl: "https://platform.toloka.ai",
  apiKey: process.env.TOLOKA_API_KEY!,
});

const training = await client.trainings.updateMetadata({
  trainingId: "trg.019a0000-0000-7000-8000-000000000001",
  name: "Text classifier v2"
});
console.log(training);

{
  "id": "trg.019a0000-0000-7000-8000-000000000001",
  "projectId": "pp.019a0000-0000-7000-8000-000000000001",
  "modelId": null,
  "name": "<string>",
  "configuration": {
    "method": "sft",
    "source": {
      "type": "base_model",
      "baseModelId": "qwen3-4b"
    },
    "loraRank": 8,
    "epochs": 1,
    "learningRate": 0.00001,
    "batchSize": 8,
    "maxSequenceLength": 64,
    "training": {
      "datasetId": "ds.019a0000-0000-7000-8000-000000000001",
      "itemFilter": {
        "fieldValues": [
          {}
        ],
        "fieldSources": [
          {}
        ],
        "missingRevisionFields": [
          "df.019a0000-0000-7000-8000-000000000001"
        ]
      }
    }
  },
  "archivedAt": null,
  "createdAt": "2026-01-01T00:00:00Z",
  "updatedAt": "2026-01-01T00:00:00Z"
}

Send a new name or archive status. Returns the updated training without changing its configuration.

Signature

Method signature on client.trainings:

updateMetadata(args: {
  trainingId: string;
  name?: string;
  archived?: false | true;
}): Promise<Training>;

Parameters

string

required

Training draft ID with prefix trg..

string

New name for the training; omit to keep the current name.

false | true

Whether to archive the training; omit to keep its current archive state.

Returns

Promise<Training> A project-owned mutable Training draft. The output Model is bound on the first Training Run.

TrainingId

required

Training draft ID with prefix trg..

ProjectId

required

Project ID with prefix pp..

ModelId | null

required

Output model ID with prefix mdl., or null before the first training run.

string

required

Name of the training draft.

TrainingConfiguration

required

A normalized method-specific Training draft.

Show method: "sft"

"sft"

required

Training method; sft learns a LoRA adapter from prompt and target examples.

TrainingModelSource

required

The exact Base Model or Model Version used as the source.

Show type: "base_model"

"base_model"

required

Training source: base_model uses a catalog model; model_version uses a published project model version.

"qwen3-4b" | "qwen3-8b" | "qwen3-32b" | "qwen3-235b"

required

Catalog base model identifier.

Show type: "model_version"

"model_version"

required

Training source: base_model uses a catalog model; model_version uses a published project model version.

string

required

Source model version ID with prefix mdv..

string

The dataset slice to train on. It must freeze a split field. Training uses its train parts and validation uses its validate parts at the slice cutoff. Rows marked as test by any split field are excluded, except validation rows in a part with both validate and test roles in the selected split field.

string

The split field to use, identified by field ID. Required when the slice freezes more than one split field.

string[]

The train parts to learn on. Defaults to every part with the train role.

8 | 16 | 32

required

LoRA adapter rank: 8, 16, or 32. Defaults to 16. Alpha is twice the rank.

number

required

Passes over the training selection, 1 through 5. The cost estimate grows linearly with epochs.

number

required

Optimizer learning rate, 0.00001 through 0.001. The default is 0.00015.

8 | 16 | 32 | 64

required

Sequences per optimizer step across all GPUs: 8, 16, 32, or 64. The default is 8.

number

required

Token budget per training example, prompt and target together: 64 through 16384. The cost estimate is blocked when the longest example exceeds it. Defaults: 12288 for SFT, 3072 for gisting.

TrainingDatasetSelection

required

A Dataset and normalized filter selected for one Training input purpose.

Show Fields

string

required

Dataset ID with prefix ds. containing the selected examples.

TrainingDatasetItemFilter

required

Conditions selecting dataset rows; omitted conditions do not restrict the selection.See the HTTP endpoint for the complete schema.

TrainingDatasetSelection

A Dataset and normalized filter selected for one Training input purpose.

Show Fields

string

required

Dataset ID with prefix ds. containing the selected examples.

TrainingDatasetItemFilter

required

Conditions selecting dataset rows; omitted conditions do not restrict the selection.See the HTTP endpoint for the complete schema.

Show method: "gisting"

"gisting"

required

Training method; gisting learns tokens that replace a reusable prompt.

TrainingModelSource

required

The exact Base Model or Model Version used as the source.

Show type: "base_model"

"base_model"

required

Training source: base_model uses a catalog model; model_version uses a published project model version.

"qwen3-4b" | "qwen3-8b" | "qwen3-32b" | "qwen3-235b"

required

Catalog base model identifier.

Show type: "model_version"

"model_version"

required

Training source: base_model uses a catalog model; model_version uses a published project model version.

string

required

Source model version ID with prefix mdv..

string

The split field to use, identified by field ID. Required when the slice freezes more than one split field.

string[]

The train parts to learn on. Defaults to every part with the train role.

string

required

Project file ID with prefix ppf. containing the finalized UTF-8 prompt to compress.

8 | 32 | 64 | "1/4" | "1/8" | "1/16" | "1/32"

required

Number of learned tokens that replace the prompt: 8, 32, 64, or a fraction of the prompt length: “1/4”, “1/8”, “1/16”, or “1/32”. Fractions are rounded up to a whole token and limited to 8 through 256 tokens. Defaults to “1/8”.

number

required

Passes over the training selection, 1 through 5. The cost estimate grows linearly with epochs.

number

required

Optimizer learning rate for the soft tokens, 0.001 through 0.3. The default is 0.03.

8 | 16 | 32 | 64

required

Sequences per optimizer step across all GPUs: 8, 16, 32, or 64. The default is 8.

number

required

Token budget per training example, prompt and target together: 64 through 16384. The cost estimate is blocked when the longest example exceeds it. Defaults: 12288 for SFT, 3072 for gisting.

TrainingDatasetSelection

required

A Dataset and normalized filter selected for one Training input purpose.

Show Fields

string

required

Dataset ID with prefix ds. containing the selected examples.

TrainingDatasetItemFilter

required

Conditions selecting dataset rows; omitted conditions do not restrict the selection.See the HTTP endpoint for the complete schema.

TrainingDatasetSelection

A Dataset and normalized filter selected for one Training input purpose.

Show Fields

string

required

Dataset ID with prefix ds. containing the selected examples.

TrainingDatasetItemFilter

required

Conditions selecting dataset rows; omitted conditions do not restrict the selection.See the HTTP endpoint for the complete schema.

string | null

required

Time the training was archived, as an ISO 8601 timestamp, or null while it is active.

string

required

Time the training was created, as an ISO 8601 timestamp.

string

required

Time the training was last updated, as an ISO 8601 timestamp.

Errors

HTTP errors throw PotatoApiError. The endpoint documents these error responses:

Status	Description
409	An active Training with this name already exists
429	Rate limit exceeded

HTTP endpoint: PATCH /trainings/{trainingId}.

import { createClient } from "@toloka-platform/sdk";

const client = createClient({
  baseUrl: "https://platform.toloka.ai",
  apiKey: process.env.TOLOKA_API_KEY!,
});

const training = await client.trainings.updateMetadata({
  trainingId: "trg.019a0000-0000-7000-8000-000000000001",
  name: "Text classifier v2"
});
console.log(training);

Nested values in this example are abbreviated. The HTTP endpoint has the complete schema.

{
  "id": "trg.019a0000-0000-7000-8000-000000000001",
  "projectId": "pp.019a0000-0000-7000-8000-000000000001",
  "modelId": null,
  "name": "<string>",
  "configuration": {
    "method": "sft",
    "source": {
      "type": "base_model",
      "baseModelId": "qwen3-4b"
    },
    "loraRank": 8,
    "epochs": 1,
    "learningRate": 0.00001,
    "batchSize": 8,
    "maxSequenceLength": 64,
    "training": {
      "datasetId": "ds.019a0000-0000-7000-8000-000000000001",
      "itemFilter": {
        "fieldValues": [
          {}
        ],
        "fieldSources": [
          {}
        ],
        "missingRevisionFields": [
          "df.019a0000-0000-7000-8000-000000000001"
        ]
      }
    }
  },
  "archivedAt": null,
  "createdAt": "2026-01-01T00:00:00Z",
  "updatedAt": "2026-01-01T00:00:00Z"
}

Reference

TypeScript SDK

CLI

HTTP API

client.trainings.updateMetadata()

Signature

Parameters

Returns

Errors

​Signature

​Parameters

​Returns

​Errors

Signature

Parameters

Returns

Errors