> ## Documentation Index > Fetch the complete documentation index at: https://docs.compute.prentis.ai/llms.txt > Use this file to discover all available pages before exploring further. # Create a supervised fine-tuning job > Start supervised fine-tuning: the model learns to answer the way the last assistant turn of each example does. The job is accepted in state JOB_STATE_QUEUED and runs in the background -- a 201 means it has been accepted, not that it has started. If the job cannot be allowed to run (for example, your organization has not accepted the data processing addendum), it is refused here and none of your data leaves. ## What happens after the 201 The job is accepted, not started. It moves `QUEUED` → `RUNNING` → `SUCCEEDED`, and on success `outputModel` names the new model in your account, which you serve on a dedicated deployment (see [Fine-tuning](/fine-tuning#5-serve-it)). Poll [Get a supervised fine-tuning job](/api-reference/get-supervised-fine-tuning-job), or watch it in the console's **Training** page -- it is the same job. Send an `Idempotency-Key` and a retried request returns the job the first one created instead of starting a second, billable run. ## OpenAPI ````yaml openapi/prentis-training.openapi.yaml POST /accounts/{account}/supervisedFineTuningJobs openapi: 3.1.0 info: title: Prentis Training API version: 0.1.0 summary: >- Fine-tune models on your own data, with the same API key you use for inference. description: > Upload a dataset, start a fine-tuning job on it, and watch it train -- from code, with the API key you already use for inference. The jobs and datasets are the same ones the console shows: anything started here appears there, and the other way round. Every path is under `/v1/accounts/{account}`, where `{account}` is the account the key belongs to. A key reaches its own account only. A finished job produces a model in your account (`accounts/{account}/models/{outputModelId}`), listed under Custom models in the console. To call it, deploy it on a dedicated deployment and send inference requests to the deployment (`accounts/{account}/deployments/{deployment}`). servers: - url: https://compute.prentis.ai/v1 description: Production security: - bearerAuth: [] tags: - name: training - name: datasets paths: /accounts/{account}/supervisedFineTuningJobs: post: tags: - training summary: Create a supervised fine-tuning job description: >- Start supervised fine-tuning: the model learns to answer the way the last assistant turn of each example does. The job is accepted in state JOB_STATE_QUEUED and runs in the background -- a 201 means it has been accepted, not that it has started. If the job cannot be allowed to run (for example, your organization has not accepted the data processing addendum), it is refused here and none of your data leaves. operationId: createSupervisedFineTuningJob parameters: - $ref: '#/components/parameters/Account' - $ref: '#/components/parameters/IdempotencyKey' requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/TrainingJobDraft' responses: '201': headers: x-request-id: $ref: '#/components/headers/x-request-id' content: application/json: schema: $ref: '#/components/schemas/TrainingJob' example: name: accounts/my-account/trainingJobs/01K7AX3V9Q2M8T4R6Y0B1C5D7E jobId: 01K7AX3V9Q2M8T4R6Y0B1C5D7E jobType: JOB_TYPE_SFT state: JOB_STATE_QUEUED baseModel: accounts/maas/models/your-base-model inputDatasetVersion: accounts/my-account/datasets/support-chats/versions/1 config: outputModelId: support-lora-v1 loraRank: 16 learningRate: 0.0001 epochs: 2 estimatedCost: amount: '1.93' currency: USD execution: thirdParty: false createTime: '2026-10-10T08:00:00Z' description: Created. '400': $ref: '#/components/responses/Error400' '401': $ref: '#/components/responses/Error401' '402': $ref: '#/components/responses/Error402' '403': $ref: '#/components/responses/Error403' '404': $ref: '#/components/responses/Error404' '409': $ref: '#/components/responses/Error409' '429': $ref: '#/components/responses/Error429' '500': $ref: '#/components/responses/Error500' '503': $ref: '#/components/responses/Error503' x-codeSamples: - lang: curl label: cURL source: >- curl https://compute.prentis.ai/v1/accounts/my-account/supervisedFineTuningJobs \ -H "Authorization: Bearer $PRENTIS_API_KEY" \ -H "Content-Type: application/json" \ -H "Idempotency-Key: support-lora-v1" \ -d '{ "baseModel": "accounts/maas/models/your-base-model", "inputDatasetVersion": "accounts/my-account/datasets/support-chats", "config": { "outputModelId": "support-lora-v1", "epochs": 2, "learningRate": "1e-4", "loraRank": 16 } }' - lang: python label: Python source: >- import os import requests BASE = "https://compute.prentis.ai/v1/accounts/my-account" HEADERS = {"Authorization": f"Bearer {os.environ['PRENTIS_API_KEY']}"} job = requests.post( f"{BASE}/supervisedFineTuningJobs", headers={**HEADERS, "Idempotency-Key": "support-lora-v1"}, json={ "baseModel": "accounts/maas/models/your-base-model", "inputDatasetVersion": "accounts/my-account/datasets/support-chats", "config": {"outputModelId": "support-lora-v1", "epochs": 2, "learningRate": "1e-4"}, }, ) job.raise_for_status() print(job.json()["name"], job.json()["state"]) components: parameters: Account: name: account in: path required: true schema: type: string maxLength: 64 description: >- Your account id -- the one in the console's address bar (my-account for this key). It must be the account the key belongs to. IdempotencyKey: name: Idempotency-Key in: header required: false schema: type: string maxLength: 255 description: >- Your own key for making a create safe to retry (up to 255 characters). Sending the same key again returns the job the first request created instead of starting a second one. schemas: TrainingJobDraft: type: object required: - baseModel - inputDatasetVersion - config properties: baseModel: type: string maxLength: 256 description: >- The model to fine-tune, as a full resource name (accounts/maas/models/your-base-model). Only base models offered for training are accepted -- the console's Training page lists them. The model to fine-tune, as a full resource name (accounts/maas/models/your-base-model). Only base models offered for training are accepted -- the console's Training page lists them. The model to fine-tune, as a full resource name (accounts/maas/models/your-base-model). Only base models offered for training are accepted -- the console's Training page lists them. The model to fine-tune, as a full resource name (accounts/maas/models/your-base-model). Only base models offered for training are accepted -- the console's Training page lists them. inputDatasetVersion: type: string maxLength: 256 description: >- The training data: a dataset (accounts/{account}/datasets/{dataset}) to use its latest version, or one version (…/versions/{n}) to pin it. The dataset must be READY. The preference data: a dataset to use its latest version, or one version to pin it. Each row needs a prompt and two answers -- see the dataset format guide. The dataset must be READY. The training data: a dataset (accounts/{account}/datasets/{dataset}) to use its latest version, or one version (…/versions/{n}) to pin it. The dataset must be READY. The training data: a dataset (accounts/{account}/datasets/{dataset}) to use its latest version, or one version (…/versions/{n}) to pin it. The dataset must be READY. evaluationDatasetVersion: type: string maxLength: 256 description: >- Accepted so that the body of a create can be sent unchanged, but not used: the estimate covers the training data only. Optional held-out data to measure evaluation loss on, in the same form as inputDatasetVersion. Leave it empty and set config.evalAutoCarveoutBasisPoints to hold out a share of the training data instead, or leave both empty for no evaluation. Accepted so that the body of a create can be sent unchanged, but not used: the estimate covers the training data only. Optional held-out data to measure evaluation loss on, in the same form as inputDatasetVersion. Leave it empty and set config.evalAutoCarveoutBasisPoints to hold out a share of the training data instead, or leave both empty for no evaluation. config: $ref: '#/components/schemas/TrainingConfig' description: >- How to train. outputModelId is required: the id of the model the job will create, which must not already exist in your account. The rest are optional -- loraRank, learningRate (a string, e.g. "1e-4"), epochs (1-100), batchSize, maxContextLength (longer examples are skipped and counted, never truncated), earlyStop, evalAutoCarveoutBasisPoints (0-9999, in hundredths of a percent), lrSchedule (constant, cosine or linear_decay), warmupSteps, and dpoBeta (preference jobs only; a string, default "0.1"). Fixed once the job is created. How to train. outputModelId is required: the id of the model the job will create, which must not already exist in your account. The rest are optional -- loraRank, learningRate (a string, e.g. "1e-4"), epochs (1-100), batchSize, maxContextLength (longer examples are skipped and counted, never truncated), earlyStop, evalAutoCarveoutBasisPoints (0-9999, in hundredths of a percent), lrSchedule (constant, cosine or linear_decay), warmupSteps, and dpoBeta (preference jobs only; a string, default "0.1"). Fixed once the job is created. How to train. outputModelId is required: the id of the model the job will create, which must not already exist in your account. The rest are optional -- loraRank, learningRate (a string, e.g. "1e-4"), epochs (1-100), batchSize, maxContextLength (longer examples are skipped and counted, never truncated), earlyStop, evalAutoCarveoutBasisPoints (0-9999, in hundredths of a percent), lrSchedule (constant, cosine or linear_decay), warmupSteps, and dpoBeta (preference jobs only; a string, default "0.1"). Fixed once the job is created. How to train. outputModelId is required: the id of the model the job will create, which must not already exist in your account. The rest are optional -- loraRank, learningRate (a string, e.g. "1e-4"), epochs (1-100), batchSize, maxContextLength (longer examples are skipped and counted, never truncated), earlyStop, evalAutoCarveoutBasisPoints (0-9999, in hundredths of a percent), lrSchedule (constant, cosine or linear_decay), warmupSteps, and dpoBeta (preference jobs only; a string, default "0.1"). Fixed once the job is created. TrainingJob: type: object properties: name: type: string jobId: type: string jobType: type: string enum: - JOB_TYPE_SFT - JOB_TYPE_DPO state: type: string enum: - JOB_STATE_QUEUED - JOB_STATE_RUNNING - JOB_STATE_CHECKPOINTING - JOB_STATE_FINALIZING - JOB_STATE_SUCCEEDED - JOB_STATE_FAILED - JOB_STATE_CANCELLED - JOB_STATE_EXPIRED stateDetail: type: string errorClass: type: string baseModel: type: string inputDatasetVersion: type: string evaluationDatasetVersion: type: string outputModel: type: string config: $ref: '#/components/schemas/TrainingConfig' progress: $ref: '#/components/schemas/JobProgress' estimatedCost: allOf: - $ref: '#/components/schemas/Money' execution: $ref: '#/components/schemas/JobExecution' createdBy: type: string createTime: type: string format: date-time startTime: type: string format: date-time completeTime: type: string format: date-time TrainingConfig: type: object required: - outputModelId properties: outputModelId: type: string maxLength: 64 loraRank: type: integer learningRate: type: string epochs: type: integer batchSize: type: integer maxContextLength: type: integer earlyStop: type: boolean evalAutoCarveoutBasisPoints: type: integer minimum: 0 maximum: 9999 lrSchedule: type: string warmupSteps: type: integer dpoBeta: type: string JobProgress: type: object properties: percent: type: integer minimum: 0 maximum: 100 epoch: type: integer totalEpochs: type: integer processedTokens: type: string format: int64 updateTime: type: string format: date-time step: type: string format: int64 totalSteps: type: string format: int64 recent: type: array maxItems: 200 items: $ref: '#/components/schemas/TrainingMetricPoint' skippedExamples: type: string format: int64 Money: type: object properties: amount: type: string currency: type: string JobExecution: type: object properties: providerDisplayName: type: string thirdParty: type: boolean ErrorResponse: type: object required: - error properties: error: type: object required: - message - type - code properties: message: type: string type: type: string enum: - invalid_request_error - authentication_error - permission_error - not_found_error - rate_limit_error - server_error - service_unavailable - billing_error code: type: string enum: - INVALID_REQUEST - INVALID_API_KEY - OPERATION_NOT_ALLOWED - TENANT_SUSPENDED - AGREEMENT_REQUIRED - DATA_RESIDENCY_VIOLATION - PROVIDER_NOT_ALLOWED - PROVIDER_SERVICE_UNAVAILABLE - INSUFFICIENT_BALANCE - PAYMENT_REQUIRED - MODEL_NOT_FOUND - MODEL_ALREADY_EXISTS - DATASET_NOT_FOUND - DATASET_ALREADY_EXISTS - DATASET_STATE_INVALID - DATASET_IN_USE - DATASET_TOO_LARGE - JSONL_LINE_NOT_OBJECT - JSONL_MISSING_FIELD - JSONL_ENCODING - JOB_NOT_FOUND - JOB_STATE_INVALID - TRAINING_RUN_ABANDONED - TRAINING_TOKENIZE_FAILED - TRAINING_BASE_NOT_EXPORTABLE - IDEMPOTENCY_CONFLICT - QUOTA_EXCEEDED - INTERNAL - STORAGE_UNAVAILABLE param: type: - string - 'null' TrainingMetricPoint: type: object properties: step: type: string format: int64 epoch: type: integer loss: type: string evalLoss: type: string learningRate: type: string time: type: string format: date-time headers: x-request-id: schema: type: string required: true description: >- On every response, successes and errors alike. Quote it when you ask us about a call. Retry-After: schema: type: integer minimum: 0 description: How many seconds to wait before retrying, on the responses that set it. responses: Error400: headers: x-request-id: $ref: '#/components/headers/x-request-id' content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' description: >- `error.code` is one of: `DATASET_STATE_INVALID`, `INVALID_REQUEST`, `JSONL_ENCODING`, `JSONL_LINE_NOT_OBJECT`, `JSONL_MISSING_FIELD`, `TRAINING_BASE_NOT_EXPORTABLE`, `TRAINING_TOKENIZE_FAILED`. See the Errors page for what each one means, whether it is safe to retry, and whether it is billed. Error401: headers: x-request-id: $ref: '#/components/headers/x-request-id' content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' description: >- `error.code` is one of: `INVALID_API_KEY`. See the Errors page for what each one means, whether it is safe to retry, and whether it is billed. Error402: headers: x-request-id: $ref: '#/components/headers/x-request-id' content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' description: >- `error.code` is one of: `INSUFFICIENT_BALANCE`, `PAYMENT_REQUIRED`. See the Errors page for what each one means, whether it is safe to retry, and whether it is billed. Error403: headers: x-request-id: $ref: '#/components/headers/x-request-id' content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' description: >- `error.code` is one of: `AGREEMENT_REQUIRED`, `DATA_RESIDENCY_VIOLATION`, `OPERATION_NOT_ALLOWED`, `PROVIDER_NOT_ALLOWED`, `PROVIDER_SERVICE_UNAVAILABLE`, `TENANT_SUSPENDED`. See the Errors page for what each one means, whether it is safe to retry, and whether it is billed. Error404: headers: x-request-id: $ref: '#/components/headers/x-request-id' content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' description: >- `error.code` is one of: `DATASET_NOT_FOUND`, `JOB_NOT_FOUND`, `MODEL_NOT_FOUND`. See the Errors page for what each one means, whether it is safe to retry, and whether it is billed. Error409: headers: x-request-id: $ref: '#/components/headers/x-request-id' content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' description: >- `error.code` is one of: `DATASET_ALREADY_EXISTS`, `DATASET_IN_USE`, `IDEMPOTENCY_CONFLICT`, `JOB_STATE_INVALID`, `MODEL_ALREADY_EXISTS`. See the Errors page for what each one means, whether it is safe to retry, and whether it is billed. Error429: headers: x-request-id: $ref: '#/components/headers/x-request-id' Retry-After: $ref: '#/components/headers/Retry-After' content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' description: >- `error.code` is one of: `QUOTA_EXCEEDED`. See the Errors page for what each one means, whether it is safe to retry, and whether it is billed. Error500: headers: x-request-id: $ref: '#/components/headers/x-request-id' content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' description: >- `error.code` is one of: `INTERNAL`, `TRAINING_RUN_ABANDONED`. See the Errors page for what each one means, whether it is safe to retry, and whether it is billed. Error503: headers: x-request-id: $ref: '#/components/headers/x-request-id' Retry-After: $ref: '#/components/headers/Retry-After' content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' description: >- `error.code` is one of: `STORAGE_UNAVAILABLE`. See the Errors page for what each one means, whether it is safe to retry, and whether it is billed. securitySchemes: bearerAuth: type: http scheme: bearer description: >- `Authorization: Bearer ` -- the same key as inference. A key reaches the account it belongs to and no other. ```` This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.