Quickstart

Package a trained checkpoint as a Dreadnode model, push it, and pull it back from Python — all from the CLI.

Prerequisites

• The Dreadnode CLI authenticated (dn login) — see Authentication
• Python with transformers installed when you plan to load the model back
• A model source directory: weights, tokenizer files, and any config the framework expects

1. Lay out the directory

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

A minimal model.yaml:

model.yaml

name: support-assistant
version: 0.1.0
summary: 7B assistant fine-tuned on support tickets.

name defaults to the directory name and version defaults to 0.1.0. Set them explicitly — the registry record is easier to read. framework is inferred from the file extensions (.safetensors wins); see Publishing for the full inference rules and the manifest reference for every field.

2. Inspect locally

dn model inspect ./support-assistant

[email protected]
  framework:    safetensors
  task:         text-generation
  architecture: LlamaForCausalLM

Files
┃ Path                        ┃
┇ model.safetensors           ┇
┇ config.json                 ┇
┇ tokenizer.json              ┇
┇ tokenizer_config.json       ┇
┇ special_tokens_map.json     ┇

inspect reads model.yaml, hashes every file, and prints the manifest the registry would record. It runs entirely locally — no API call — so use it as a pre-flight before pushing.

3. Push to the registry

dn model push ./support-assistant

Pushed acme/[email protected] (sha256:ab3c7f...)

The version goes to your organization (acme here) and is visible only to that org by default. The qualified name is org/name@version. Re-pushing a directory with a single changed file uploads only that file.

4. Load it from code

import dreadnode as dn
from dreadnode.models import Model

dn.pull_package(["model://acme/support-assistant:0.1.0"])
model = Model("acme/support-assistant", version="0.1.0")

hf_model = model.to_hf(torch_dtype="bfloat16", device_map="auto")
tokenizer = model.tokenizer()

pull_package downloads the version you just pushed; Model(...) opens it by name. See Using in code for the difference between pull_package/load_package and for serving the weights through a generator.

5. Bump a version

Edit the directory, bump version in model.yaml, and push again:

# model.yaml
version: 0.2.0

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

Older versions stay in the registry. When you’re ready to promote, attach metrics with dn model metrics and move the champion alias:

dn model metrics [email protected] intent_accuracy=0.873 f1=0.86
dn model alias [email protected] champion

See Versions & metrics for the comparison, promotion, and retirement flow.

What to reach for next

• LoRA adapters, custom frameworks, full catalog metadata → Publishing
• Compare releases, attach metrics, move aliases → Versions & metrics
• Pull, load, and feed the model into an evaluation → Using in code
• Browse what’s already in the registry → Catalog
• Every CLI verb → dn model