> ## Documentation Index > Fetch the complete documentation index at: https://docs.priorlabs.ai/llms.txt > Use this file to discover all available pages before exploring further. # Fit a Model > **Deprecated:** Prefer the TabPFN client (`tabpfn-client`) or `POST /tabpfn/fit` after preparing uploads. This multipart `/v1/fit` surface is legacy. See the [TabPFN-3 changelog](/changelog/tabpfn-3). Uploads and fits a TabPFN model on your training data. The API automatically handles preprocessing and stores a reference to your trained context (not the model weights). You can use either a single dataset file (with the target column included) or separate feature and label files. ## OpenAPI ````yaml /api-reference/openapi.json post /v1/fit openapi: 3.1.0 info: title: TabPFN API description: >- Prior Labs TabPFN API. **Prefer [tabpfn-client](https://github.com/PriorLabs/tabpfn-client)**. Current integration surface: **`/tabpfn/*` JSON routes** (prepare uploads, fit, predict, limits). **`/v1/*` multipart routes are deprecated.** See the [Changelog](/changelog). version: 2.0.0 contact: name: Prior Labs email: hello@priorlabs.ai servers: - url: https://api.priorlabs.ai description: Production TabPFN API (`/tabpfn/*` current; `/v1/*` deprecated) security: - BearerAuth: [] paths: /v1/fit: post: tags: - Training summary: Fit a Model description: >- **Deprecated:** Prefer the TabPFN client (`tabpfn-client`) or `POST /tabpfn/fit` after preparing uploads. This multipart `/v1/fit` surface is legacy. See the [TabPFN-3 changelog](/changelog/tabpfn-3). Uploads and fits a TabPFN model on your training data. The API automatically handles preprocessing and stores a reference to your trained context (not the model weights). You can use either a single dataset file (with the target column included) or separate feature and label files. operationId: fit_v1_deprecated requestBody: required: true content: multipart/form-data: schema: type: object required: - data properties: data: type: string description: >- A JSON string defining the training configuration. **Supported Systems**: - `["preprocessing"]` - Applies skrub preprocessing, - `["text"]` - Adds text embeddings for text columns. **Default**: `["preprocessing", "text"]`. **Supported Config Parameters**: - `n_estimators` (`int`, 1-10) - Number of ensemble estimators, - `softmax_temperature` (`float`) - Temperature for softmax scaling, - `average_before_softmax` (`bool`) - Average before softmax, - `ignore_pretraining_limits` (`bool`) - Ignore pretraining limits, - `random_state` (`int`) - Random seed for reproducibility. dataset_file: type: string format: binary description: >- **Option 1:** CSV file containing both features (`X_train`) and labels (`y_train`). Use this when you have **all data in a single file**. features_file: type: string format: binary description: >- **Option 2:**: CSV file containing only feature columns (`X_train`). Must be used together with `labels_file`. labels_file: type: string format: binary description: >- **Option 2:** CSV file containing only the target/label column (`y_train`). Must be used together with `features_file`. oneOf: - required: - data - dataset_file - required: - data - features_file - labels_file examples: single_dataset: summary: Single CSV file (features + target) value: data: '{"task": "classification", "schema": {"target": "churn"}}' dataset_file: '[binary data]' separate_files: summary: Separate features and labels files value: data: '{"task": "regression", "schema": {"target": "price"}}' features_file: '[binary data]' labels_file: '[binary data]' responses: '200': description: >- Model fitted successfully — returns a model ID for later prediction calls. content: application/json: schema: $ref: '#/components/schemas/FitResponse' example: model_id: 123e4567-e89b-12d3-a456-426614174000 task: classification '400': description: Invalid request — missing data or malformed input. content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' example: code: error.bad_request detail: Invalid request data retryable: false support: https://discord.com/invite/VJRuU3bSxt '401': description: >- Unauthorized — authentication required or credentials invalid. **Possible causes:** - Missing or malformed `Authorization` header - Invalid or expired JWT token - User not found (token references a deleted account) - JWT decode errors (JWEDecodeError, JWTDecodeError, JWTClaimsError) **Examples:** - Missing token: `{"detail": "Not authenticated"}` - Invalid credentials: `{"detail": "Could not validate credentials"}` content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' examples: missing_token: value: code: auth.unauthorized detail: Not authenticated retryable: false support: https://discord.com/invite/VJRuU3bSxt invalid_credentials: value: code: auth.unauthorized detail: Could not validate credentials retryable: false support: https://discord.com/invite/VJRuU3bSxt expired_token: value: code: auth.unauthorized detail: Invalid or expired JWT token retryable: false support: https://discord.com/invite/VJRuU3bSxt '403': description: >- Forbidden — user account not verified or insufficient permissions. **Possible causes:** - Valid token for an unverified user - Account not yet verified via email **Example response:** `{"code": "auth.forbidden", "detail": "User account not verified", "retryable": false, "support": "https://discord.com/invite/VJRuU3bSxt"}` content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' example: code: auth.forbidden detail: User account not verified retryable: false support: https://discord.com/invite/VJRuU3bSxt '422': description: Validation error — one or more fields are incorrectly formatted. content: application/json: schema: $ref: '#/components/schemas/ValidationError' deprecated: true security: - BearerAuth: [] x-codeSamples: - lang: python label: Single CSV upload source: |- import os, json, requests # Define your dataset path train_path = "train.csv" # Get your API key from the environment api_key = os.getenv("PRIORLABS_API_KEY") headers = {"Authorization": f"Bearer {api_key}"} # Upload your training dataset to /v1/fit payload = { "task": "classification", "schema": { "target": "churn", "description": "Customer churn dataset" } } files = { "data": (None, json.dumps(payload), "application/json"), "dataset_file": (train_path, open(train_path, "rb")), } fit_response = requests.post( "https://api.priorlabs.ai/v1/fit", headers=headers, files=files, ) model_id = fit_response.json().get("model_id") print(f"✅ Model trained: {model_id}") - lang: python label: Separate features + labels source: |- import os, json, requests # Define your file paths X_train_path = "X_train.csv" # features only y_train_path = "y_train.csv" # labels only # Get your API key from the environment api_key = os.getenv("PRIORLABS_API_KEY") headers = {"Authorization": f"Bearer {api_key}"} # Upload your training dataset with separate files payload = { "task": "regression", "schema": { "target": "price", "description": "House price prediction dataset" } } files = { "data": (None, json.dumps(payload), "application/json"), "features_file": (X_train_path, open(X_train_path, "rb")), "labels_file": (y_train_path, open(y_train_path, "rb")), } fit_response = requests.post( "https://api.priorlabs.ai/v1/fit", headers=headers, files=files, ) model_id = fit_response.json().get("model_id") print(f"✅ Model trained: {model_id}") components: schemas: FitResponse: type: object required: - model_id - task properties: model_id: type: string format: uuid description: Unique identifier for the fitted model (used for prediction calls). task: $ref: '#/components/schemas/PredictionTask' example: model_id: 123e4567-e89b-12d3-a456-426614174000 task: classification ErrorResponse: type: object required: - code - detail - retryable - support properties: code: type: string description: Error category code (e.g., auth.unauthorized, rate.limit.exceeded) example: auth.unauthorized detail: type: string description: Human-readable error message describing what went wrong. example: Invalid authentication credentials retryable: type: boolean description: Indicates whether the request can be retried. example: false support: type: string description: URL to get support for this error. example: https://discord.com/invite/VJRuU3bSxt example: code: auth.unauthorized detail: Invalid authentication credentials retryable: false support: https://discord.com/invite/VJRuU3bSxt ValidationError: type: object required: - code - detail - errors - retryable - support properties: code: type: string description: Error category code for validation errors example: validation.failed detail: type: string description: Human-readable validation error message example: Validation error for FitRequest errors: type: array description: Array of field-level validation errors items: type: object properties: field: type: string description: Field name where the error occurred example: task message: type: string description: Error message for this field example: field required retryable: type: boolean description: Indicates whether the request can be retried example: false support: type: string description: URL to get support for this error example: https://discord.com/invite/VJRuU3bSxt example: code: validation.failed detail: Validation error for FitRequest errors: - field: task message: field required - field: systems message: >- Invalid system: invalid_system. Must be one of: ['preprocessing', 'text'] retryable: false support: https://discord.com/invite/VJRuU3bSxt PredictionTask: type: string enum: - classification - regression description: >- Specifies the type of task to perform — either classification or regression. securitySchemes: BearerAuth: type: http scheme: bearer bearerFormat: JWT description: >- Bearer token for authentication, obtained after signing up and generating an API key. ````