> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ironlabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Data format

# Custom Router Training Data Format

## Structure

```json theme={null}
{
  "problems": [
    {
      "problem_key": "string — unique ID per problem",
      "problem":     "string — raw input text the router classifies",
      "correct_models": ["model/string", "..."]
    }
  ]
}
```

`incorrect_models` is optional and ignored during training. Only `correct_models` is used as the multi-label target.

## Validation (src/ml\_tasks.py:138-141)

Problems are silently dropped if missing any of: `problem_key`, `problem`, `correct_models`.

Minimum 10 valid problems required. Fewer → `ValueError: No valid 'problems' found`.

## Model string format

Use OpenRouter model IDs, e.g.:

* `openai/gpt-4o`
* `openai/gpt-4o-mini`
* `anthropic/claude-3-haiku`
* `google/gemini-2.5-flash`
* `qwen/qwen3-coder`

The trained router outputs these exact strings as predictions.

## Quality tips

* Include diverse problem types — router learns from variation
* At least 2–3 models per problem (multi-label signal)
* Problems where only one model ever succeeds add weak signal
* Problems where no model succeeds are dropped (unsolvable filter)
* 100+ problems recommended for production accuracy

## Hosting

Data must be at a publicly accessible URL. Options:

* **Cloudflare R2** — upload with boto3, use public bucket URL
* **GitHub Gist** — raw URL works
* **Any HTTP server** — must return `Content-Type: application/json`