Create Training Job with PIN API

Create a training job through the Aria PIN API by specifying the job type, base model, dataset, LoRA configuration, and hyperparameters. The endpoint returns immediately with a queued job identifier.

Endpoint

POST /v1/jobs Send an Authorization: Bearer <api_key> header with every request.

Request body

string

required

Training type: sft, opd, pretrain, distill, qat, grpo, dpo, kto, orpo, simpo, or ppo.

string

required

Hugging Face model identifier or local path for the trainable base.

string

Frozen teacher model identifier. Required when type is opd or distill, ignored otherwise.

object

required

LoRA configuration object.

Show properties

string

required

lora, qlora, or none.

integer

required

LoRA rank (for example, 16).

integer

required

LoRA alpha (for example, 32).

number

Dropout rate.

string[]

Target module names (for example, ["q_proj", "v_proj"]).

string

Quantization method when method=qlora, such as 4bit_nf4.

string

required

Dataset identifier registered in PIN.

object

required

Training hyperparameters. Common fields include epochs, lr. Type-specific fields include distill_alpha, temperature, fp8_recipe, max_tokens, qat_group_size, group_size, and beta.

object

Optional auto-intervention configuration with rules, cooldown, and enabled flag.

Response

string

Unique identifier for the created job.

string

Initial status, typically queued.

Example

curl -X POST http://host:8001/v1/jobs \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "type": "opd",
    "base_model": "thinkingmachines/Inkling",
    "teacher_model": "Qwen/Qwen2.5-1.5B-Instruct",
    "lora": {"method": "lora", "rank": 16, "alpha": 32},
    "dataset_id": "ds_xxx",
    "hyperparams": {"epochs": 1, "lr": 5e-5, "distill_alpha": 0.7, "temperature": 3.0}
  }'

Response

{
  "code": 0,
  "data": {
    "job_id": "job_abc123",
    "status": "queued"
  },
  "message": ""
}

PIN API

ROUTER API

AGENT API

Create Training Job with PIN API

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