Models

A Dreadnode model is a directory with a model.yaml manifest that the platform packages, versions, and serves back by reference. Publish the full weights from a training run, a LoRA adapter for the same base model, or a vendored third-party checkpoint — then pin that version from inference code, downstream training, or an evaluation.

support-assistant/
  model.yaml
  model.safetensors
  tokenizer.json
  tokenizer_config.json
  special_tokens_map.json

dn model push ./support-assistant    # → acme/[email protected]

Note

“Models” in the registry sense — published weight artifacts — are different from the inference models you pick at session or evaluation time (openai/gpt-5, dn/claude-opus-4-6, anthropic/claude-opus-4-6). Those are selected per run; see Agent & model for how the TUI picker works. This section is about stored artifacts you publish yourself.

The lifecycle

1. Train or adapt a model elsewhere — hosted training jobs, a local fine-tune, a vendor checkpoint you want to curate.
2. Author the directory: a model.yaml, the weights, a tokenizer if the model uses one.
3. Inspect before publishing — dn model inspect ./path reads model.yaml and previews the artifact list.
4. Push to the registry with dn model push or dn.push_model(...).
5. Compare and annotate — attach metrics, tag versions with aliases like champion or staging, pick the release to promote.
6. Consume from inference code, downstream training, or evaluation harnesses by pinning org/name@version.

What a model artifact can contain

The registry is agnostic about what you publish — it tracks the bytes, the manifest, and the metadata. Common shapes:

Shape	Typical manifest settings
Full weights	framework: safetensors, architecture, task, tokenizer files.
LoRA adapter	framework: safetensors, base_model: <ref>, adapter files only.
ONNX export	framework: onnx, one or more .onnx files.
Quantized checkpoint	Framework matching the checkpoint format, size_category set.
Curated third-party checkpoint	base_model: <upstream-ref>, license set.

Picking a version

Every version carries a framework, a file list, optional metrics, and optional aliases. Aliases (champion, staging, latest-stable) float across versions so humans can promote without rewriting downstream configs; automation should still pin org/name@version for reproducibility.

Related surfaces

Quickstart

Publishing

Versions & metrics

Catalog

Using in code

Manifest reference

Full CLI: dn model. The Hub surfaces the same registry with filters, version comparison, and metrics charts. Hosted training writes weights into workspace storage — see Training → Overview for emitting a checkpoint and then publishing it here.