> ## Documentation Index > Fetch the complete documentation index at: https://nixtla.io/docs/llms.txt > Use this file to discover all available pages before exploring further. # Foundational Time Series Model Multi Series Finetuning > Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference. ## OpenAPI ````yaml /openapi.json post /v2/finetune openapi: 3.1.0 info: title: Nixtla Forecast API description: >- API for TimeGPT forecast. Just send your data as json and get results. We do the heavy lifting. version: 0.2.8 servers: - url: https://api.nixtla.io security: [] paths: /v2/finetune: post: summary: Foundational Time Series Model Multi Series Finetuning description: >- Fine-tune the large time model to your data and save it for later use. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains the ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference. operationId: v2_finetune_v2_finetune_post requestBody: content: application/json: schema: $ref: '#/components/schemas/FinetuneInput' examples: - finetune_steps: 10 freq: MS model: timegpt-1 series: sizes: - 36 'y': - 0 - 1 - 2 - 3 - 4 - 5 - 6 - 7 - 8 - 9 - 10 - 11 - 12 - 13 - 14 - 15 - 16 - 17 - 18 - 19 - 20 - 21 - 22 - 23 - 24 - 25 - 26 - 27 - 28 - 29 - 30 - 31 - 32 - 33 - 34 - 35 required: true responses: '200': description: Successful Response content: application/json: schema: $ref: '#/components/schemas/FinetuneOutput' '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' security: - HTTPBearer: [] components: schemas: FinetuneInput: properties: series: $ref: '#/components/schemas/SeriesWithFutureExogenous' freq: type: string title: Freq description: >- The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available. model: type: string title: Model description: >- Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data. default: timegpt-1 finetune_steps: type: integer minimum: 0 title: Finetune Steps description: >- The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning. default: 10 finetune_loss: type: string enum: - default - mae - mse - rmse - mape - smape - poisson title: Finetune Loss description: >- The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape', 'poisson']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers. default: default finetune_depth: type: integer enum: - 1 - 2 - 3 - 4 - 5 title: Finetune Depth description: >- The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning, and 5 means that the entire model is finetuned. Note that this parameter is only effective for timegpt-1 and timegpt-1-long-horizon models, meanwhile it has no effect on the other models. By default, the value is set to 1. default: 1 output_model_id: anyOf: - type: string pattern: ^[a-zA-Z0-9\-_]{1,36}$ - type: 'null' title: Output Model Id description: ID to assign to the finetuned model finetuned_model_id: anyOf: - type: string pattern: ^[a-zA-Z0-9\-_]{1,36}$ - type: 'null' title: Finetuned Model Id description: ID of previously finetuned model hist_exog: anyOf: - items: type: integer minimum: 0 type: array - type: 'null' title: Hist Exog description: Zero-based indices of the exogenous features to treat as historical. multivariate: type: boolean title: Multivariate description: >- Compute multivariate predictions across a batch of multiple time series. Requires all time series with overlapping dates. Note that this is only effective for timegpt-2.1 model and it has no effect on the other models. By default, the value is set to False. default: false model_parameters: anyOf: - additionalProperties: true type: object - type: 'null' title: Model Parameters description: >- Optional dictionary of parameters to customize the behavior of the large time model. type: object required: - series - freq title: FinetuneInput FinetuneOutput: properties: input_tokens: type: integer minimum: 0 title: Input Tokens output_tokens: type: integer minimum: 0 title: Output Tokens finetune_tokens: type: integer minimum: 0 title: Finetune Tokens finetuned_model_id: type: string pattern: ^[a-zA-Z0-9\-_]{1,36}$ title: Finetuned Model Id type: object required: - input_tokens - output_tokens - finetune_tokens - finetuned_model_id title: FinetuneOutput HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError SeriesWithFutureExogenous: properties: X_future: anyOf: - items: items: anyOf: - type: number - type: string type: array type: array - type: 'null' title: X Future description: >- Future values of the exogenous features. Each feature must be a list of size number of series times the forecast horizon (h). X: anyOf: - items: items: anyOf: - type: number - type: string type: array type: array - type: 'null' title: X description: >- Historic values of the exogenous features. Each feature must be a list of the same size as the target (y). categorical_exog: anyOf: - items: type: integer minimum: 0 type: array - type: 'null' title: Categorical Exog description: >- Zero-based indices of the columns in X that are categorical features. 'y': items: type: number type: array title: 'Y' description: Historic values of the target. sizes: items: type: integer type: array title: Sizes description: Sizes of the individual series. type: object required: - 'y' - sizes title: SeriesWithFutureExogenous ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: type: string title: Error Type input: title: Input ctx: type: object title: Context type: object required: - loc - msg - type title: ValidationError securitySchemes: HTTPBearer: type: http description: HTTPBearer scheme: bearer ````