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Fine-tuning in Pioneer adapts a base model to your specific task and domain using your labeled dataset. You submit a training job through the API, Pioneer handles the compute, and you get back a trained model you can call for inference or download. The whole process is asynchronous — you start the job, then poll until it finishes.

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Training job lifecycle

Every training job moves through a fixed sequence of states:
1

requested

Your job has been accepted and is queued for execution. Pioneer is allocating compute.
2

running

Training is actively executing. You can stream logs to monitor progress.
3

complete

Training finished successfully. Metrics (F1, precision, recall) are available on the job record, and checkpoints are ready to download.
A job may also end in failed (an error occurred during training) or stopped (you manually cancelled it with POST /felix/training-jobs/:id/stop).

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Key parameters

ParameterRequiredDescription
model_nameYesA name for your trained model, used to identify it in your account.
base_modelYesThe model ID to fine-tune. Use a value from GET /base-models or a checkpoint UUID from a previous job.
datasetsYesAn array of dataset objects: [{"name": "your-dataset-name"}].
training_typeNo"lora" (default, parameter-efficient) or "full" (all weights).
nr_epochsNoNumber of training epochs. Defaults to a reasonable value for the base model.
learning_rateNoLearning rate. Omit to use the default for the chosen base model.
base_model is required. Omitting it returns a 422 validation error. The value must be a model ID from GET /base-models or a checkpoint UUID — not a free-form string.

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Starting a training job

curl -X POST https://api.pioneer.ai/felix/training-jobs \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "my-ner-model",
    "base_model": "fastino/gliner2-base-v1",
    "datasets": [{"name": "my-ner-dataset"}],
    "training_type": "lora",
    "nr_epochs": 5,
    "learning_rate": 5e-5
  }'
The response returns immediately with a job ID and initial status: 
{
  "id": "job_abc123",
  "status": "requested"
}
Save the id — you’ll use it to poll status, retrieve metrics, and run inference against your trained model.

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Polling status and reading metrics

Poll the job endpoint until status is complete or failed: 
curl https://api.pioneer.ai/felix/training-jobs/job_abc123 \
  -H "X-API-Key: YOUR_API_KEY"
When training completes, the response includes evaluation metrics measured on your dataset: 
{
  "id": "job_abc123",
  "status": "complete",
  "metrics": {
    "f1": 0.94,
    "precision": 0.96,
    "recall": 0.92
  }
}
To stream logs while the job is running: 
curl https://api.pioneer.ai/felix/training-jobs/job_abc123/logs \
  -H "X-API-Key: YOUR_API_KEY"

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Stopping a job

If you need to cancel a running job: 
curl -X POST https://api.pioneer.ai/felix/training-jobs/job_abc123/stop \
  -H "X-API-Key: YOUR_API_KEY"
The job status changes to stopped. Partial checkpoints saved before the stop may still be available.

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Checkpoints and downloading weights

Pioneer saves checkpoints during training. You can list them at any point after the job starts: 
curl https://api.pioneer.ai/felix/training-jobs/job_abc123/checkpoints \
  -H "X-API-Key: YOUR_API_KEY"
To download the final trained weights: 
curl https://api.pioneer.ai/felix/training-jobs/job_abc123/download \
  -H "X-API-Key: YOUR_API_KEY" \
  --output my-model-weights.zip
You can also use a checkpoint UUID as the base_model value in a new training job to continue training from that checkpoint.

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Training endpoints summary

MethodEndpointDescription
POST/felix/training-jobsStart a new training job
GET/felix/training-jobsList all training jobs
GET/felix/training-jobs/:idGet job status and metrics
GET/felix/training-jobs/:id/logsStream training logs
GET/felix/training-jobs/:id/checkpointsList saved checkpoints
GET/felix/training-jobs/:id/downloadDownload trained weights
POST/felix/training-jobs/:id/stopStop a running job
DELETE/felix/training-jobs/:idDelete a training job record