Create a config training job - Fastino API Docs

POST

https://api.fastino.ai

training

jobs

curl -X POST https://api.fastino.ai/v1/training/jobs \
  -H "X-API-Key: $FASTINO_API_KEY" \
  -H "Idempotency-Key: YOUR_UNIQUE_RETRY_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "my-model-name",
    "base_model": "fastino/gliner2-base-v1",
    "datasets": [{"name": "my-ready-dataset"}],
    "training_type": "lora",
    "nr_epochs": 5,
    "learning_rate": 5e-5,
    "validation_data_percentage": 0.2
  }'

{
  "id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
  "type": "config",
  "model_name": "my-model-name",
  "base_model": "fastino/gliner2-base-v1",
  "status": "requested",
  "training_type": "lora",
  "nr_epochs": 5,
  "learning_rate": 5e-5,
  "validation_data_percentage": 0.2
}

POST /v1/training/jobs Starts asynchronous managed training (config job) and returns its job record. You provide a dataset and configuration, and Fastino runs the training loop. For interactive training (interactive job), where your application controls each training step, follow the Decoder Quickstart, which creates an interactive job through POST /v1/training/jobs/create-model or the supported Tinker SDK. This endpoint does not create interactive jobs. After creation, both job types use their canonical IDs with the documented /v1/training/jobs/{job_id} lifecycle operations. config jobs train encoder models that are returned by GET /v1/base-models?supports_training=true and have a compatible config job dispatch route. Decoder base models return 400; train decoders with an interactive job instead. This endpoint is limited to 20 requests per minute per user.

Request

string

required

Your Fastino API key.

string

Client-generated retry key, 1–255 characters; a UUID is recommended. Repeating the same key and body returns the original job. Reusing the key with a different body returns 422.

string

required

Display name for the trained model. Length: 1–100 characters.

string

required

A trainable encoder ID returned by GET /v1/base-models?supports_training=true. The selected training_type must have a compatible dispatch route. Checkpoint UUIDs and decoder base models return 400.

object[]

required

One or more uploaded dataset references. Each item requires name and may include version; omitted versions resolve to the latest version.

string

default:"lora"

lora or full. The selected base model must support the requested type.

number

default:"0.2"

Fraction held out for validation, from 0 to 0.9 in increments of 0.01.

integer

default:"100"

Maximum epoch count, from 1 to 10000; early stopping can finish sooner.

number

Peak learning rate from 1e-8 to 1. Omit it to use the selected model’s training recipe.

integer

Per-device batch size from 1 to 256. Omit it to use the model’s default_training_batch_size from GET /v1/base-models?supports_training=true. A value above the model’s max_training_batch_size, when the catalog publishes one, returns 422. A GLiNER batch that doesn’t fit the training GPU is reduced to the largest that fits; the job’s batch_size shows the size it runs with.

integer

Optional reproducibility seed from 0 through 2147483647. Set provider_name to modal when using it. A seed reduces one source of variation but does not guarantee bit-identical GPU runs.

string

Project UUID. When omitted, Fastino associates the job with the caller’s default project.

Show Additional training parameters

integer

default:"100"

Save a checkpoint every N steps, from 1 to 2147483647.

boolean

default:"false"

Persist a structured training profile artifact.

string

Optional Weights & Biases API key. Treat it as a secret and never put it in source control, logs, or examples.

integer

LoRA rank from 1 to 256. Omit it to use the model recipe.

integer

Positive LoRA alpha, up to 2147483647. Omit it to use the model recipe.

number

LoRA dropout from 0 (inclusive) to 1 (exclusive). Omit it to use the model recipe.

number

Warmup fraction from 0 to 1. Use this field to configure warmup for GLiNER jobs.

integer

Absolute warmup-step count, from 1 to 2147483647. GLiNER jobs reject this field with 422 unsupported_encoder_scheduling; use warmup_ratio.

string

Learning-rate schedule: constant, linear, or cosine. GLiNER uses linear when this field is omitted; only an explicit value is forwarded.

number

Accepted for request compatibility but not forwarded to GLiNER training. GLiNER uses its container default of 0.01.

integer

default:"3"

Validation epochs without improvement before stopping, from 0 to 10000. Set 0 to disable.

number

default:"0.0001"

Non-negative minimum validation-loss improvement that counts as progress.

string

Pin a compatible training provider. Omit it for automatic selection.

number

GLiNER encoder learning rate from 1e-8 to 1. Falls back to learning_rate.

number

GLiNER task-head learning rate from 1e-8 to 1. Falls back to learning_rate.

integer

Positive mini-batch accumulation count, up to 2147483647.

boolean

Enable GLiNER dataset auto-sizing.

integer

GLiNER auto-sizing lower bound, from 1 to 2147483647.

integer

GLiNER auto-sizing upper bound, from 1 to 2147483647.

integer

GLiNER auto-sizing scale per label, from 1 to 2147483647.

integer

Minimum GLiNER optimizer steps, from 0 to 2147483647.

integer

GLiNER2 periodic validation interval, from 1 to 2147483647 optimizer steps. Omit it to validate only at epoch end.

integer

GLiNER2 per-dataset cap for periodic validation, from 1 to 2147483647. Omit it for no cap.

integer

GLiNER maximum input length, from 1 to 2147483647 tokens. Omit it to use the model context limit. A value above the model’s max_training_length from GET /v1/base-models?supports_training=true returns 422.

integer

GLiNER2 maximum retained gold spans per query, from 1 to 1024. Omit it to use 32.

string

GLiNER2 behavior when gold spans exceed max_gold_per_query: raise, truncate_with_warning, or skip_sample. Omit it to use raise.

string

default:"sft"

Only sft is supported for config jobs. Omit it to use the default.

curl -X POST https://api.fastino.ai/v1/training/jobs \
  -H "X-API-Key: $FASTINO_API_KEY" \
  -H "Idempotency-Key: YOUR_UNIQUE_RETRY_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "my-model-name",
    "base_model": "fastino/gliner2-base-v1",
    "datasets": [{"name": "my-ready-dataset"}],
    "training_type": "lora",
    "nr_epochs": 5,
    "learning_rate": 5e-5,
    "validation_data_percentage": 0.2
  }'

Response

Returns 200 with the full training-job record, including a UUID id and initial status. The example below is abbreviated; the actual response includes all fields defined by TrainingJobResponse.

{
  "id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
  "type": "config",
  "model_name": "my-model-name",
  "base_model": "fastino/gliner2-base-v1",
  "status": "requested",
  "training_type": "lora",
  "nr_epochs": 5,
  "learning_rate": 5e-5,
  "validation_data_percentage": 0.2
}

Save the id from this response. You will use it to poll status, retrieve metrics and artifacts, and run inference against the trained model. A malformed body returns 422. A valid but unavailable model, dataset, training type, or provider combination returns a request-specific 4xx error. Creation can return these coded conflicts:

Status	Meaning
`402`	The account has never been funded or has exhausted its credits.
`403`	A payment method, account requirement, or the caller’s team role blocks creation.
`409 duplicate_training_submission`	An identical active job was submitted in the previous 60 seconds. Poll the `job_id` from the error instead of resubmitting.
`409 idempotency_key_job_deleted`	The job originally created with this idempotency key was deleted. Use a new key for a new job.
`409`	A referenced dataset retired between validation and job insertion. Resolve the dataset and retry with a new key.
`422 idempotency_key_reused`	The same `Idempotency-Key` was used with a different request body. Use the original body to replay, or a new key for a new job.
`422 catalog_training_shape_exceeded`	`batch_size` is above the model’s `max_training_batch_size`, or `max_length` is above its `max_training_length`. Read both from `GET /v1/base-models?supports_training=true` and send values within them.
`429`	The request rate or active `config` job limit was exceeded. A user can have at most 25 active `config` jobs.
`503`	Billing state could not be checked or all training providers are at capacity. Retry later with the same `Idempotency-Key`.

The same Idempotency-Key and identical body replay the original job instead of creating another. A changed body with the same key returns 422. The 60-second identical-submission check is separate from idempotency-key replay. After that window, sending the same body without reusing the original key creates and bills another job. If the requested model name is already taken, the next job receives a suffix such as <name>_2. A decoder base model returns 400:

{
  "error": {
    "message": "Decoder training on this route is retired: 'Qwen/Qwen3.6-35B-A3B' cannot be trained here. Train decoders step by step with the training job routes under /v1/training/jobs (see https://docs.fastino.ai/training/quickstart).",
    "type": "invalid_request_error",
    "param": null,
    "code": null
  }
}

To train a decoder, create an interactive job with POST /v1/training/jobs/create-model and follow the Decoder Quickstart.

Authorization

Requires the Owner, Admin, or Editor team role. Other roles receive 403 Forbidden.

curl -X POST https://api.fastino.ai/v1/training/jobs \
  -H "X-API-Key: $FASTINO_API_KEY" \
  -H "Idempotency-Key: YOUR_UNIQUE_RETRY_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "my-model-name",
    "base_model": "fastino/gliner2-base-v1",
    "datasets": [{"name": "my-ready-dataset"}],
    "training_type": "lora",
    "nr_epochs": 5,
    "learning_rate": 5e-5,
    "validation_data_percentage": 0.2
  }'

{
  "id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
  "type": "config",
  "model_name": "my-model-name",
  "base_model": "fastino/gliner2-base-v1",
  "status": "requested",
  "training_type": "lora",
  "nr_epochs": 5,
  "learning_rate": 5e-5,
  "validation_data_percentage": 0.2
}

​Request

​Response

​Authorization

Request

Response

Authorization