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Publishing

Package a trained model as a Dreadnode artifact, write model.yaml, and push a version to the registry.

Publishing a model is two decisions: what goes into the directory, and which framework the registry should record. Everything downstream — version comparison, metric attachment, pulling — operates on what you push here.

The directory shape

Section titled “The directory shape”
support-assistant/
  model.yaml               # required — the manifest
  model.safetensors
  config.json
  tokenizer.json
  tokenizer_config.json
  special_tokens_map.json

Every file under the directory (except model.yaml and OS junk like .DS_Store) becomes an artifact. Constrain the set explicitly with files: when the directory contains things you don’t want published.

See the manifest reference for every accepted field.

Minimum manifest

Section titled “Minimum manifest”
name: support-assistant
version: 0.1.0

framework is inferred from the file extensions present, in priority order: any .safetensors → safetensors; otherwise any .onnx → onnx; otherwise any .pt/.pth/.bin → pytorch; otherwise safetensors. A directory with both a PyTorch checkpoint and a safetensors file resolves to safetensors.

Full fine-tune

Section titled “Full fine-tune”

Fill in the catalog metadata so the Hub record is useful to someone who didn’t train the model:

name: support-assistant
version: 1.0.0
summary: 7B assistant fine-tuned on support tickets.
framework: safetensors
architecture: LlamaForCausalLM
task: text-generation
base_model: meta-llama/Llama-3.1-8B-Instruct
dataset_refs:
  - acme/[email protected]
license: apache-2.0
language: [en]
tags: [assistant, support, sft]
task_categories: [conversational]
size_category: 1-7B

base_model and dataset_refs form the training provenance chain — downstream consumers follow the links to understand what went into the weights.

LoRA adapter

Section titled “LoRA adapter”

LoRAs are published the same way as a full model, with a smaller file set and a base_model pointer:

name: support-assistant-lora
version: 0.3.0
summary: LoRA adapter for Llama-3.1-8B-Instruct, rank 16.
framework: safetensors
base_model: meta-llama/Llama-3.1-8B-Instruct
dataset_refs:
  - acme/[email protected]
files:
  - adapter_config.json
  - adapter_model.safetensors
  - tokenizer.json
  - tokenizer_config.json
  - special_tokens_map.json

Explicit files: prevents accidentally shipping a full checkpoint alongside the adapter.

ONNX export

Section titled “ONNX export”
name: support-classifier-onnx
version: 0.1.0
framework: onnx
task: sequence-classification
architecture: DistilBertForSequenceClassification

ONNX models are usually single-file. Let the discovery rules pick it up, or declare it explicitly with files:.

Inspect before pushing

Section titled “Inspect before pushing”
Terminal window
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 each file, and prints the manifest the registry would record. Use it as a local pre-flight.

Push to the registry

Section titled “Push to the registry”
Terminal window
dn model push ./support-assistant
Pushed acme/[email protected] (sha256:ab3c7f...)

The CLI validates the manifest, hashes every artifact, uploads only the files the registry doesn’t already have, and writes the versioned manifest. Re-publishing a checkpoint with a single changed file ships only that file.

Override the registry name with --name, or cross-publish into another organization you have write access to:

Terminal window
dn model push ./support-assistant --name acme-research/support-assistant

Dry-run

Section titled “Dry-run”
Terminal window
dn model push ./support-assistant --skip-upload

Runs every local step and stops before the HTTP upload. Useful for CI validation before paying the bytes.

Publish from Python

Section titled “Publish from Python”
import dreadnode as dn


dn.configure(server="https://app.dreadnode.io", api_key="dn_...", organization="acme")


result = dn.push_model("./support-assistant")
print(result.package_name, result.package_version)
# acme/support-assistant 1.0.0

dn.push_model accepts the same skip_upload and name arguments as the CLI. The returned PushResult carries manifest_digest, blobs_uploaded, and blobs_skipped.

Control visibility

Section titled “Control visibility”

Models are private to your organization by default. Visibility is name-level — every version of acme/support-assistant shares one setting.

ActionCommand
Make the model publicdn model publish support-assistant
Restrict it againdn model unpublish support-assistant
Publish at push timedn model push ./... --publish

publish and unpublish accept multiple names and reject version-qualified refs — the switch flips the whole family.

What to reach for next

Section titled “What to reach for next”