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dria batch

Run parallel inference on a JSONL file. Dria automatically distributes work across available models and handles retries with exponential backoff.

Basic usage

Input format

Each line is a JSON object with a prompt field. Optional id and attachment fields:

Output format

Results are written as JSONL. Each line contains the model used, output text, and token count:
Failed items include an error field instead of output.

Auto model selection

When you don’t specify -m, Dria:
  1. Fetches all available models and their node counts
  2. Classifies each prompt by content type (text, vision, audio) based on the attachment
  3. Distributes prompts across models proportionally to node availability
  4. If a model goes down (503), automatically falls back to the next best model
This means your batch jobs are resilient to individual model failures.

Structured output in batch

Apply structured output to all prompts:
Or with a JSON schema file:

Options