Interaktives Modell erstellen

POST

https://api.fastino.ai

training

jobs

create-model

curl -X POST "https://api.fastino.ai/v1/training/jobs/create-model" \
  -H "X-API-Key: $FASTINO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "base_model": "Qwen/Qwen3.6-35B-A3B",
    "lora_config": {"rank": 32}
  }'

{
  "job_id": "SESSION_ID:train:0",
  "type": "interactive",
  "base_model": "Qwen/Qwen3.6-35B-A3B",
  "future_id": "FUTURE_ID",
  "load_future_id": null
}

POST /v1/training/jobs/create-model Creates an interactive LoRA training job on a decoder base model, or a new job seeded from a saved training state. Your application then sends each forward-backward pass, optimizer step, checkpoint, and sample. Native JSON clients do not send seq_id values or heartbeats. For managed training from an uploaded dataset, use POST /v1/training/jobs. The job is ready once its create future completes. Read it with GET /v1/training/jobs/{job_id}/futures/{future_id}.

Request

string

erforderlich

Your Fastino API key.

Send exactly one of base_model and from_checkpoint.

string

nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16, nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16, or Qwen/Qwen3.6-35B-A3B.

string

The path that save-weights returned for one of your jobs, such as tinker://<job_id>/weights/<name>. The new job takes that checkpoint’s base model and LoRA settings, so omit lora_config. Sampler checkpoints are rejected.

boolean

Standard:"false"

Set load_optimizer with from_checkpoint to restore the optimizer state.

object

LoRA settings for a new job.

Anzeigen properties

integer

Standard:"32"

LoRA rank, from 1 through 32.

integer

Adapter initialization seed.

boolean

Standard:"true"

Train attention layers.

boolean

Standard:"true"

Train MLP layers.

boolean

Standard:"true"

Train the unembedding layer.

object

Optimizer family. Only {"type": "adamw"} is served, which is the default.

string

Project the job and its usage belong to. Defaults to your default project.

object

Your own JSON metadata for the job.

curl -X POST "https://api.fastino.ai/v1/training/jobs/create-model" \
  -H "X-API-Key: $FASTINO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "base_model": "Qwen/Qwen3.6-35B-A3B",
    "lora_config": {"rank": 32}
  }'

Response

string

Interactive job ID. Use it with every /v1/training/jobs/{job_id} route.

string

Always interactive.

string

Base model of the job.

string

The job’s create future.

string | null

With from_checkpoint, the future that loads the checkpoint.

{
  "job_id": "SESSION_ID:train:0",
  "type": "interactive",
  "base_model": "Qwen/Qwen3.6-35B-A3B",
  "future_id": "FUTURE_ID",
  "load_future_id": null
}

Errors

Status	Meaning
`400`	The model isn’t served, the rank is out of range, `from_checkpoint` is a sampler checkpoint, or `lora_config` was sent with `from_checkpoint`.
`402`	The team is out of credits or has no valid payment setup.
`404`	Interactive training isn’t available to the team, or the checkpoint doesn’t exist.
`422`	Both or neither of `base_model` and `from_checkpoint` were sent, or the body has an unknown field.

⌘I

curl -X POST "https://api.fastino.ai/v1/training/jobs/create-model" \
  -H "X-API-Key: $FASTINO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "base_model": "Qwen/Qwen3.6-35B-A3B",
    "lora_config": {"rank": 32}
  }'

{
  "job_id": "SESSION_ID:train:0",
  "type": "interactive",
  "base_model": "Qwen/Qwen3.6-35B-A3B",
  "future_id": "FUTURE_ID",
  "load_future_id": null
}

Inferenz

Training

Interaktives Modell erstellen

Request

Response

Errors

​Request

​Response

​Errors

Request

Response

Errors