# import-model

Walks an agent through porting a Hugging Face architecture into MAX: read the config, build the layers, verify against the reference.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1400 installs · ★ 206
- Author: modular
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/modular/skills/tree/HEAD/import-model/SKILL.md
- Updated: 2026-10-25
- HTML: https://affpapa.org/en/nevibecode/skills/import-model

## What it does

The skill fires when you want a new architecture running in MAX from a Hugging Face model ID. The agent reads `config.json` and the model's `modeling_<type>.py`, picks a similar ModuleV3 architecture as a donor, and scaffolds from it. Every layer becomes a `Module` subclass from `max.experimental.nn` with a `forward()` over `Tensor` values, and ops come from `max.experimental.functional`.

Work runs in three phases: decide and plan, implement, verify. Guards gate the transitions. One example: don't run the verification scripts until the completion criteria in `implement-graph.md` pass, because `<slug>.py` still implements the donor and the logits won't match. Helper scripts sit in `scripts/` and go through `pixi run python scripts/import_model.py …` — `inspect`, `scaffold`, `check-walls`, `gates`, `compare`.

You need a pixi env with MAX installed, network access to the Hugging Face Hub, and a GPU for serving and verification. Run everything through `pixi run`, not bare `python` or `max` on the shell PATH. If the server starts but the output is gibberish or drifts from the reference, the skill hands off to `debug-model` for the divergence hunt.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add modular/skills --skill import-model -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/import-model/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/import-model/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add modular/skills --skill import-model -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/import-model/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add modular/skills --skill import-model -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/import-model/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install modular/skills import-model
```

2. **Manually: put the files in place**
- File: `.github/skills/import-model/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add modular/skills --skill import-model -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/modular/skills --path import-model
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/import-model/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add modular/skills --skill import-model -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/modular/skills/tree/HEAD/import-model/SKILL.md

## When to use it
- port a Hugging Face architecture into MAX from its model ID
- find a similar ModuleV3 architecture to scaffold from
- implement each layer as a `max.experimental.nn` Module
- compare ported logits against the Hugging Face reference
- skip the port when MAX already registers the architecture

## Example prompts
- Import Qwen/Qwen3-8B into MAX
- Add this model to MAX, start from the olmo3 donor
- The server runs but the output is gibberish — load debug-model

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 2e905b308fd8a069.

BEGIN THIRD-PARTY FILE 2e905b308fd8a069
```text
---
name: import-model
description: >
  Use when importing a new model architecture into MAX from a Hugging Face model ID.
  Triggers on: "import a model into MAX", "add model to MAX", "bring up <HF model> in MAX".
  Workflow: inspect the Hugging Face config and modeling code, then scaffold from
  a similar ModuleV3 architecture. Implement each layer as a max.experimental.nn
  Module that matches HF, serve, then verify against the Hugging Face reference.
  When the server runs but output is wrong (gibberish, greedy mismatch,
  coherent-then-diverges), load debug-model for the divergence hunt.
compatibility: Requires pixi env with MAX installed, network access to Hugging Face Hub, and a GPU for serving/verification.
metadata:
  argument-hint: "[Hugging Face model ID, for example 'Qwen/Qwen3-8B']"
---

# Import a model into MAX

**Input:** a Hugging Face model ID (`$ARGUMENTS`).

Copy [references/template.md](references/template.md) to track this port as
you work through the phases. Its file references are paths from this skill's
root, so they still resolve after you copy it elsewhere.

Porting a model to MAX means writing a ModuleV3 model that performs the same
computation as the model's `modeling_<type>.py` in Hugging Face
`transformers`. You then compile it with the released weights and verify that
the outputs match. ModuleV3 is MAX's model API. Layers are `Module` subclasses
from `max.experimental.nn` with a `forward()` over `Tensor` values. Ops come
from `max.experimental.functional` (imported as `F`). The pipeline builds the
module under `F.lazy()` and compiles it with `compile(weights=...)`.
Single-GPU, tensor-parallel, and expert-parallel models all use ModuleV3.
Multi-GPU models shard through a `DeviceMesh`.

The workflow phases are **decide and plan**, **implement**, and **verify**.
Phase 1 is reading and planning. Phase 2 is the port: implement every divergent
sublayer in the module. Phase 3 is verification, only after implementation is
complete. Guards (preconditions that halt the workflow) gate the transitions
between activities. They aren't steps of their own.

**Anti-pattern:** running `scaffold.py`, tweaking `arch.py`, and serving
while `<slug>.py` still implements the donor (`llama3_modulev3`, `olmo3`, …).
Logit verification fails on that port because `<slug>.py` still runs the
donor architecture. Don't run verification scripts until
[implement-graph.md](references/implement-graph.md) completion criteria pass.

Each phase links to references with the details. Read the reference for the
activity you're on, not all of them upfront.

**Environment:** run every command through the pixi env that has MAX
installed (`pixi run python …`, `pixi run max serve …`). Run them from the
```
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