What does this skill do, and when should you use it?
This skill helps NeMo AutoModel developers add or modify model architecture support. It starts with Hugging Face config.json discovery and proceeds through implementation, weight adaptation, registry updates, example configuration, testing, documentation, and parity validation. It covers dense LLMs, mixture-of-experts models, and vision-language models, with explicit safeguards for expert mappings and numerical equivalence. It is one skill in NVIDIA/skills, a 324-skill monorepo installed through the repository’s CLI flow.
Instructs an agent to inspect a Hugging Face config.json and classify the architecture as dense LLM, MoE, or VLM; identify model.py, state_dict_adapter.py, and optional config, layer, and RoPE files under components/models/<name>/; update model and custom-config registrations in _transformers/registry.py; map router, expert, text, and vision weights; create an example YAML and tiny-config, forward-shape, adapter round-trip, layer-equivalence, loss, and parity tests; and update model-coverage documentation.
- A NeMo AutoModel developer adding a new Hugging Face causal-LM architecture can use it to determine the required files, registry entries, and validation gates.
- A model engineer onboarding an MoE checkpoint with routers, shared experts, or combined projections can use it to audit expert ordering and state-dict mappings.
- A VLM developer adding a model with vision_config, text_config, and a conditional-generation architecture can use it to check the vision tower, projector, processor assumptions, and image-text tests.
- A maintainer changing custom attention, RoPE, normalization, MLP, or precision-sensitive parameters can use it to identify capability declarations and equivalence tests.
How do you install this skill?
- Do not treat the benchmark PASS or high scores as independent verification for this review; run adapter round-trip, layer-equivalence, image-text, and end-to-end parity tests against the target NeMo AutoModel version.
- Before fetching config.json or checkpoints from Hugging Face, verify network reachability, model provenance, licensing, dependency versions, and user authorization; no mainland-China fallback procedure is documented.
- The guide references several companion files and repository paths; if they are absent from an installation, those references may be unusable.
- Shell / CLI
- Network access
- Local filesystem
Hugging Face TransformersPyTorchNeMo AutoModel
Install the skill using the command documented in the repository README: npx skills add nvidia/skills --skill nemo-automodel-model-onboarding --yes. You can target an agent explicitly, for example: npx skills add nvidia/skills --skill nemo-automodel-model-onboarding --agent codex.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/NVIDIA/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/nemo-automodel-model-onboarding" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Add support for a new Hugging Face MoE architecture in NeMo AutoModel, including state-dict adaptation, registration, and tests.
After installation, invoke it for a model-architecture onboarding task, such as: “Add support for a new Hugging Face MoE architecture in NeMo AutoModel, including state-dict adaptation, registration, and tests.” The source does not document a standalone executable or dedicated runtime command; code changes and test execution depend on the target repository environment.
What are this skill's strengths and limitations?
- Covers the workflow from config.json discovery through end-to-end parity testing.
- Provides concrete classification signals and file locations for dense LLM, MoE, and VLM onboarding.
- Highlights commonly missed checks including expert ordering, tied weights, tiny configurations, and numerical equivalence.
- Defines a clear boundary that excludes standalone recipe, launcher, and distributed-strategy questions.
- It is an implementation guide, not a runnable onboarding generator or complete model implementation.
- It assumes access to Hugging Face configuration and NeMo AutoModel source code, plus a PyTorch test environment.
- The source provides no platform test matrix, performance measurements, or complete list of already supported models.
- The supplied repository metadata says NOASSERTION for the repository license, while this skill declares Apache-2.0; verify the applicable license before adoption.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| NeMo AutoModel Model Onboarding this page ✓ NVIDIA · Official | 60 · Recommended | ★ 3.8k | today | Apache-2.0 |
| MoE Dispatcher Selection Guide ✓ NVIDIA · Official | 52 · Use with care | ★ 3.8k | today | Apache-2.0 |
| Earth2Studio Diagnostic Builder ✓ NVIDIA · Official | 56 · Use with care | ★ 3.8k | today | Apache-2.0 |
| NeMo MBridge MoE Communication Overlap ✓ NVIDIA · Official | 56 · Use with care | ★ 3.8k | today | Apache-2.0 |
| Hugging Face CLI Skill | 55 · Use with care | ★ 38k | 16d ago | MIT |
How did FollowSkills review this skill?
The skill declares Apache-2.0, scopes its guidance to model onboarding, and includes explicit warnings around weight mapping and validation. No malware, credential theft, covert exfiltration, or destructive default was found. However, it directs users to fetch Hugging Face configuration and rely on external model repositories without explicit confirmation, data-flow disclosure, source validation, dependency security, rollback, or least-privilege guidance, so points are deducted.
The workflow, file layout, registry steps, adapter guidance, and testing requirements are detailed, with some abnormal-case warnings. However, this review is static: key paths were not executed, examples and external dependencies were not verified, and the document says five phases while specifying six. The score therefore remains conservative under the static cap.
Audience, use cases, routing boundary, positive examples, negative examples, and Dense/MoE/VLM classification signals are relatively clear. Missing are stronger handling rules for incomplete inputs, Chinese-language support, and mainland-China network reachability; the discovery path also depends on Hugging Face, so points are deducted.
The main guide, pattern references, precision guidance, checklist, skill card, evaluations, and benchmark report provide useful progressive disclosure and identify the license, author, and some update context. Versioning, changelog, maintenance ownership, dependency installation, FAQs, known limitations, and stable citation practices remain incomplete. The benchmark also records schema and author-format findings.
The skill provides actionable guidance for discovery, implementation files, registration, state-dict conversion, tests, and parity checks. Its supplied benchmark reports strong results on three positive tasks. Nevertheless, no independently reproduced output is available in this static review, and the guidance still requires engineering implementation and review, so the score is limited by the static cap.
The revision includes detailed primary skill material, checklists, evaluation tasks, and a benchmark report, giving the claims some auditability. It does not provide independently reproducible CI evidence, committed key-path test results, or corroboration from multiple sources within the supplied material, so the score is limited to the static-review ceiling.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →