Skip to main content
POST
Create a fine-tune training job

Authorizations

Authorization
string
header
required

The access token received from the authorization server in the OAuth 2.0 flow.

Body

application/json

Start a fine-tune training run from an approved dataset job.

dataset_job_id
string<uuid>
required

A COMPLETE fine-tune dataset job supplying the JSONL + base model.

name
string | null

Human-friendly job name. Defaults to '-<base_model>'.

Maximum string length: 256
adapter_type
enum<string>
default:qlora

Parameter-efficient fine-tuning method.

Available options:
lora,
qlora
hyperparameters
Hyperparameters · object

Trainer hyperparameters (epochs, learning_rate, lora_r, lora_alpha, lora_dropout, max_seq_len, ...). Trainer-side defaults fill any gaps.

gpu_count
integer | null

Override GPU count; clamped to the configured min/max.

Required range: 1 <= x <= 64
trigger_now
boolean
default:true

Trigger the Airflow DAG immediately after creation.

Response

Job created with rendered DAG.

Full state of a fine-tune training job.

job_id
string<uuid>
required
dataset_job_id
string<uuid>
required
name
string
required
domain
string
required
base_model
string
required
owner
string
required
adapter_type
enum<string>
required

Parameter-efficient fine-tuning method.

Available options:
lora,
qlora
status
enum<string>
required

Lifecycle of a fine-tune training job (mirrors the DB enum).

Available options:
ready,
queued,
running,
complete,
failed
created_at
string<date-time>
required
updated_at
string<date-time>
required
hyperparameters
Hyperparameters · object
gpu_resources
Gpu Resources · object | null
rendered_dag
string | null
airflow_dag_id
string | null
airflow_dag_url
string | null
mlflow_run_id
string | null
registered_model_name
string | null
model_version
string | null
error_message
string | null