Encoder Quickstart

This quickstart fine-tunes a GLiNER encoder with a managed config job while Fastino runs the training loop. To train a Nemotron or Qwen decoder LLM with an application-controlled training loop, use the Decoder Quickstart.

1. Authenticate

Create a Fastino account.
Open API keys.
Create a key and copy it when shown.

Store the key in an environment variable:

export FASTINO_API_KEY="fast_sk_..."

$env:FASTINO_API_KEY = "fast_sk_..."

Send this key as X-API-Key: $FASTINO_API_KEY. See Authentication for the complete authentication contract.

2. Prepare a dataset

Upload and process a training dataset, then wait until its version is ready.

3. Choose a model

Managed training supports fastino/gliner2-multi-v1 and fastino/GLiNER-2.5-Decide. Query the live catalog before submission:

curl "https://api.fastino.ai/v1/base-models?supports_training=true" \
  -H "X-API-Key: $FASTINO_API_KEY"

Save the selected model ID.

4. Create the job

curl -X POST "https://api.fastino.ai/v1/training/jobs" \
  -H "X-API-Key: $FASTINO_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: $(uuidgen)" \
  -d '{
    "model_name": "my-trained-model",
    "base_model": "MODEL_ID_FROM_CATALOG",
    "datasets": [
      {"name": "my-ready-dataset", "version": "VERSION_NUMBER"}
    ],
    "training_type": "lora"
  }'

Save the returned id. It is the job_id used by every job-specific route. Reuse the same Idempotency-Key only when retrying this exact create request.

5. Poll the job

curl "https://api.fastino.ai/v1/training/jobs/JOB_ID" \
  -H "X-API-Key: $FASTINO_API_KEY"

For config jobs:

status is the persisted lifecycle value, such as errored.
normalized_status is the stable polling value, such as failed.
is_terminal_status tells your client when to stop polling.
error_message describes a failure when available.

Continue until is_terminal_status is true, then inspect the final status, checkpoints, logs, and deployment eligibility.

Get Started

Decision Models

Entity & Classification

Training API

Inference API

Troubleshooting

SDK Integrations

Models

Account

1. Authenticate

2. Prepare a dataset

3. Choose a model

4. Create the job

5. Poll the job

Continue

Datasets

Decoder Quickstart

​1. Authenticate

​2. Prepare a dataset

​3. Choose a model

​4. Create the job

​5. Poll the job

​Continue

Datasets

Decoder Quickstart

1. Authenticate

2. Prepare a dataset

3. Choose a model

4. Create the job

5. Poll the job

Continue