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Documentation Index

Fetch the complete documentation index at: https://docs.pioneer.ai/llms.txt

Use this file to discover all available pages before exploring further.

The Pioneer REST API lets you run inference, manage datasets, start training jobs, and evaluate models programmatically. All resources live under a single base URL, and every request is authenticated with your API key. Pioneer also exposes OpenAI- and Anthropic-compatible endpoints so you can integrate with any tool that already supports those formats. Base URL: https://api.pioneer.ai

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Authentication

Every request requires an X-API-Key header. Generate your key at pioneer.ai → Settings → API Keys. See the Authentication page for full details, including key management endpoints. Follow this sequence when you’re building a fine-tuned model end to end:
1

Create or upload a dataset

Use POST /generate to generate synthetic labeled data — the response includes a job_id, poll GET /generate/jobs/{job_id} until status returns ready.To upload your own labeled data, use the three-step upload flow:
  1. Call POST /felix/datasets/upload/url to get a presigned S3 URL,
  2. PUT your file directly to S3
  3. Call POST /felix/datasets/upload/process to trigger processing.
Poll GET /felix/datasets/{name}/{version} until status is ready.
See the Datasets page for full details.
2

Start a training job

Call POST /felix/training-jobs with your model_name, datasets, and base_model. The response includes a job_id — save it for the following steps.
3

Poll training status

Call GET /felix/training-jobs/:id repeatedly until the status field is complete. Completed jobs include F1, precision, and recall metrics.
4

Run an evaluation

Call POST /felix/evaluations with your training job ID and a held-out dataset name to validate performance before you serve traffic.
5

Run inference

Call POST /inference with base_model set to your training job ID and dataset_name set to your held out dataset. You can also use any base model ID (for example fastino/gliner2-base-v1) without fine-tuning.
Replace placeholder values such as YOUR_DATASET_NAME and YOUR_TRAINING_JOB_ID with real values before running any example commands.

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Endpoint groups

Inference

Run predictions against fine-tuned or base models using the Pioneer format, or via drop-in OpenAI- and Anthropic-compatible endpoints.

Training & Data

Manage datasets, generate synthetic training data, start and monitor training jobs, and run evaluations.

Projects

Organize resources into projects, deploy trained models, and run inference against a project endpoint.

API Keys

Create, list, and revoke API keys programmatically.

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OpenAI and Anthropic compatibility

Pioneer exposes drop-in replacements for the OpenAI and Anthropic SDKs. Point your existing client at https://api.pioneer.ai/v1 and use your Pioneer API key — no other changes are required.
SDKBase URLNotes
OpenAIhttps://api.pioneer.ai/v1Set base_url; pass Pioneer-specific fields via extra_body
Anthropichttps://api.pioneer.ai/v1Set base_url; supports streaming
Pass Pioneer-specific fields like schema using extra_body (OpenAI SDK) or the equivalent extra-parameters mechanism for your client library.