Import a model into MAX

Input: a Hugging Face model ID (

$ARGUMENTS

Copy references/template.md to track this port as you work through the phases.

Porting a model to MAX means writing a MAX graph that performs the same computation as the model's

modeling_<type>.py

in Hugging Face

transformers

, then loading the released weights into that graph and verifying the outputs match.

The workflow has three phases: decide & plan, implement, verify. Phase 1 is reading and planning. Phase 2 is the port: implement every divergent sublayer in the graph. Phase 3 is verification, only after implementation is complete. Guards (preconditions that stop the line) gate the transitions between activities; they are not steps of their own.

Anti-pattern: running

scaffold.py

, tweaking

arch.py

, and serving while

<slug>.py

still implements the donor (

llama3

qwen3

, …). That is not a bring-up — logit verification will fail because the wrong architecture is running. Do not run verification scripts until 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 …

), from the skill root where

pixi.toml

lives (do not use bare

python

max

on the shell PATH):

bash

cd <path-to-skill>
pixi install
pixi run python scripts/inspect_hf.py <HF_MODEL_ID>
# Or: pixi run test-scripts   # smoke-test all scripts (no GPU)

Helper scripts live in this skill's

scripts/

directory (copy or vendor them into your repo). All helpers are also reachable through a unified dispatcher with the same argument names and exit codes:

bash

pixi run python scripts/import_model.py inspect <HF_MODEL_ID>
pixi run python scripts/import_model.py scaffold <HF_MODEL_ID> --start-from llama3 --output-dir ./
pixi run python scripts/import_model.py list-archs --match LlamaForCausalLM
pixi run python scripts/import_model.py check-walls <HF_MODEL_ID>
pixi run python scripts/import_model.py list-keys <HF_MODEL_ID> --summary
pixi run python scripts/import_model.py gates <HF_MODEL_ID> --port-dir <port_dir>/
pixi run python scripts/import_model.py compare <HF_MODEL_ID> --slug <slug> --port 8000

Port layout:

<port_dir>
— slug folder containing

arch.py

and

ARCHITECTURES

__init__.py

(usually

<output_dir>/<slug>/

). Pass this path to both

--custom-architectures

and

run_oss_gates.py --port-dir

MAX resolves

--custom-architectures <port_dir>

by adding

dirname(<port_dir>)

sys.path

and importing

basename(<port_dir>)

as the module. Passing the parent directory imports the wrong module name (e.g.

custom-arch

instead of your slug).

Import/API errors while editing: copy the donor arch under

modular/max/python/max/pipelines/architectures/<donor>/

; see pitfalls-config.md § Import and config API traps.

Phase 1 — Decide & plan

Guard: is the architecture already registered in MAX? Before writing any code, check whether MAX already registers the architecture class in your model's
config.json::architectures[0]
. If
pixi run python list_native_archs.py --match <Class>
returns a slug, run
pixi run max serve --model <HF_MODEL_ID>
and stop — no port needed. Full procedure: native-arch-check.md.

Read

config.json

Pull the config and read every field:

bash

pixi run python -c "from transformers import AutoConfig; \
  print(AutoConfig.from_pretrained('<HF_MODEL_ID>', trust_remote_code=True))"

Or use the helper, which fetches raw

config.json

from the Hub, runs the native-arch check, and prints every key mapped to the MAX API:

bash

pixi run python inspect_hf.py <HF_MODEL_ID>

Then list safetensors metadata (keys, shapes, dtypes — no weight download):

bash

pixi run python list_checkpoint_keys.py <HF_MODEL_ID> --summary

Each row is one

config.json

key →

pipeline_config.model.huggingface_config

(or

SupportedArchitecture

arch.py

for

architectures

torch_dtype

). Keys you cannot wire through

MyConfig.initialize()

are the deltas you implement in the graph. Field meanings and common deltas: read-config-json.md.

Scan for hard blockers before you commit to a port:

bash

pixi run python check_walls.py <HF_MODEL_ID>

Exit 0 → continue. Exit 1 → review recognize-walls.md. Exit 2 → stop until the wall is resolved or scoped out.

Read the model card

Open

https://huggingface.co/<HF_MODEL_ID>

and read the model card for:

The paper or blog post. Skim its architecture section — authors call out the interesting modifications (QK-norm, MLA, sliding-window attention, MoE routing) because those are what they want credit for.
"Tricks" mentioned in the card. Phrases like "we introduce", "unlike prior models", "this is the first model to" mark deltas that will bite you during implementation if you miss them now.

If the card says the model is from a known family (Llama, Mistral, Qwen, Gemma), note that; the donor-comparison activity below will start from the closest already-ported variant of that family.

If the card mentions custom CUDA kernels, custom attention with no public reference, FP8/FP4-only released weights, ALiBi, recurrence or state-space layers; see recognize-walls.md before going further. Some models can't be ported with the public MAX surface alone.

Propose a plan; accept a veto

Before any code, write a short paragraph stating what you'd do by default, then wait for the user to confirm or veto. Cover four axes (distribution shape, quantization variants, validation depth, hardware target) — all derived from what you've already read. Don't ask blank questions; state a default and let them push back.

Full guidance and an example paragraph: plan-and-veto.md.

If estimated weight bytes do not fit one GPU, read distributed-transformer.md before choosing

--start-from

— distribution shape matters more than attention family alone.

Compare with other MAX architectures

You're picking the closest already-ported MAX architecture to copy from. "Closest" means: same attention shape (dense vs. GQA vs. MLA vs. MoE), same MLP shape (gated vs. non-gated, dense vs. routed), same head layout (tied vs. untied, single Linear vs. multi-step).

List what your installed MAX registers (do not hard-code a slug list):

bash

pixi run python list_native_archs.py

Heuristic HF-signal → donor slug hints are in map-to-max.md. Quick version:

Your model	Start from
Llama 3-ish (GQA, RoPE, SwiGLU MLP)	`llama3`
Gemma-ish (RMSNorm scale, logit softcap, dual norm)	`gemma2` or `gemma3`
Qwen-ish (GQA, RoPE, may have QK-norm)	`qwen2` / `qwen3`
Mistral-ish (sliding window)	`mistral`
Phi-ish (partial RoPE)	`phi3`
MoE (sparse experts, top-k routing)	`mixtral` or `qwen3_moe`
MLA (latent KV)	`deepseekV3`

Open the chosen MAX arch's directory and read its top-level model file (usually

<slug>.py

). You're answering: which functions/classes need to change vs. stay the same when I port my model?

Now read the corresponding Hugging Face modeling file:

bash

pixi run python -c "from transformers.models.<model_type> import modeling_<model_type>; \
  print(modeling_<model_type>.__file__)"

Read the

__init__

, the attention

forward

, the MLP

forward

, the block class, and the final head. Compare each to the MAX equivalent. The reference read-modeling-code.md covers what to look for in each.

Output of this activity: a delta list — one row per real difference between HF and the donor MAX arch (attention, MLP/MoE, block wiring, head, RoPE, masks). You implement every row in Phase 2. Three or fewer structural deltas → good donor choice. Many deltas → pick a closer donor or plan to rewrite whole classes. Do not proceed to verification with an empty or "looks Llama-ish" delta list.

Phase 2 — Implement

Scaffold the file layout

scaffold.py

only copies files. It does not implement your model.

bash

pixi run python scaffold.py <HF_MODEL_ID> --start-from <max_arch_slug> --output-dir <output_dir>

This reads

architectures[0]

from the Hub

config.json

for

arch.py::name

, then copies the chosen native MAX architecture into

<output_dir>/<slug>/

as five files:

```
arch.py
```
— registration shell (verify
```
name=
```
and encoding)
```
model_config.py
```
— donor config (must be rewired during implementation)
```
model.py
```
— pipeline model shell
```
weight_adapters.py
```
— donor renames (must be rewritten for your checkpoint)
```
<slug>.py
```
— donor graph (must be edited to match HF during implementation)

After scaffold, you have a directory layout and a wrong graph. Stop here until the graph is implemented — do not serve.

Scaffold also leaves the donor's docstrings and code comments in place. Sed-renaming class names doesn't touch text that records what the file claims to do. After scaffold,

<slug>.py

opens with a docstring describing the donor; the new class claims behaviors (single-GPU support, QK-norm, post-attention norm, etc.) the new file may not have. Rewriting those docstrings is a required part of the graph implementation — not optional polish. See honest-docstrings.md for the three-sentence pattern every module docstring should follow and a mandatory audit checklist before declaring the implementation done.

Implement the graph

This is the bring-up. Phase 1 produced the config map and delta list; the implementation activity executes them in code.

Full checklist, work order, anti-patterns, and completion criteria: implement-graph.md.

In order:

model_config.py
— wire every
```
config.json
```
key from Phase 1 /
```
inspect_hf.py
```
. Set
```
get_kv_params()
```
head counts and head_dim to match HF.
weight_adapters.py
— map your checkpoint's safetensor keys to the MAX module names you will use. Run
```
list_checkpoint_keys.py
```
first; see rename-weights.md. After load, wire the coverage audit in state-dict-audit.md (especially MoE and
```
strict=False
```
tied embeddings).
<slug>.py
— for each row in the delta list, edit or replace the donor class so MAX
```
forward()
```
mirrors HF
```
forward()
```
:
- Attention (Q/K/V, RoPE, mask, GQA, softcap, …)
- MLP or MoE (activation, routing, shared experts, …)
- Decoder block (norm order and residual wiring — not interchangeable with Llama)
- Final norm and LM head (tie, logit scale, softcap)

arch.py
— confirm

name=

matches

architectures[0]

;

default_encoding

matches Hub

torch_dtype

model.py
— only if HF wraps the backbone differently (VL, multi-modal).

Read HF

modeling_<type>.py

while editing, not after verification fails. Subclass the donor only where HF and donor match; rewrite the class where the delta list said they differ.

The implementation is done when every item in implement-graph.md is checked — especially: every delta has a corresponding code change, weights load without orphan keys, and the scaffold-comment audit in honest-docstrings.md has been run with each match classified as OK / Lie / Stale. A passing audit is mandatory; declaring the implementation done without it leaves donor lies in the codebase that nothing downstream will catch.

Quick grep recipe (full classification rules in honest-docstrings.md):

bash

pixi run rg -i -n 'qwen|llama|mistral|cohere|gemma|phi|deepseek|exaone|olmo|granite|qwen3|mixtral|single-GPU|single GPU|RMSNorm|QK-norm' <port_dir>/

Your implementation-complete message must explicitly attest to the audit (e.g.

"docstrings rewritten to the three-sentence pattern; rg returns N hits, all legitimate lineage references"

). A claim without the attestation isn't a completion.

Preflight (Hub config + arch registration — run before first serve):

bash

pixi run python run_oss_gates.py <HF_MODEL_ID> --port-dir <port_dir>/

Guard: local smoke gate (mandatory before Phase 3).
pixi run max serve
cold-compiles for 5–25 minutes. Before serving, run the four local checks in serve-and-iterate.md (import smoke, graph dry-build, adapter⇄graph key diff, weights-format preflight).
run_oss_gates.py
covers walls, checkpoint metadata, and
arch.py
name/encoding — not a substitute for those four.

Phase 3 — Verify

Check if it generates coherent text

Prerequisite: graph implementation complete. Do not serve to "see what happens" during implementation — fix config, adapters, and graph first.

Sanity-check the HF reference FIRST. Run HF alone on the model card's intended prompt template, before involving MAX. If HF itself produces gibberish, your oracle is broken — fixing your port against a broken oracle wastes days.

Then serve with

pixi run max serve --model-path <HF_MODEL_ID> --custom-architectures <port_dir>

and probe with the model card's intended template (not just "The capital of France is" — that prompt is wrong for PrefixLMs and instruction-tuned models). Three possible outcomes: server crashes during load → fix config/adapters; server starts but returns garbage → divergence hunt; server returns plausible text → run at

max_tokens=64+

before celebrating.

Full HF-reference sanity check, encoder/embedding slug serve flow, and fix-test loop discipline: serve-and-iterate.md.

Layer-by-layer divergence hunt

This is the main loop. You're going to:

Read the HF reference implementation to understand what should happen at each layer.
Dump intermediate tensors from both implementations and find the first layer where they diverge.
Fix that layer.
Re-run the layer check.
Repeat until all layers match.

Read the reference implementation

Before diving into the HF source, consult the symptom table at the top of divergences.md. Match what you're observing (gibberish at token 0, divergence growing with length, output plausible but text drifts, etc.) to its candidate causes, and read every candidate listed — not just the first plausible one. Several causes produce the same symptom; the bug is the one you haven't checked yet.

Then open the HF

modeling_<type>.py

as a debugger, not a reviewer. You're looking for the specific detail you missed. Common ones:

A norm whose variant or position differs from the template
A scale factor applied somewhere (
```
hidden_states * scale
```
,
```
attn_weights / sqrt(d)
```
, MuP multipliers)
A different activation function in the MLP
A different RoPE style (split-half vs. interleaved, partial vs. full)
A boundary condition that only fires at certain layers (sliding-window vs. global attention, sink-token handling)

Easter-egg warning: HF modeling code inherits aggressively. A class named

MyModelDecoderLayer(GraniteDecoderLayer)

may inherit critical behavior from a different family entirely. Always chase inheritance up at least one level before concluding "this is just Llama with renamed fields."

The catalog of "this differs from Llama and here is how" is in divergences.md. Indexed by symptom.

Compare logits (and HF layer stats)

bash

pixi run python compare_layers.py <HF_MODEL_ID> \
  --slug <your_slug> --port 8000 \
  --prompt "The capital of France is"

Requires

pixi run max serve

with

--custom-architectures <port_dir>

on the same port.

MAX does not expose per-layer hidden states via the OpenAI API. This script:

Prints HF-only per-layer stats (embedding + each block output) as a diagnostic snapshot while you read the modeling code.
Compares top-1 logprob at the prompt between HF and MAX via
```
/v1/completions?logprobs=…
```
.

If logprobs diverge, use divergences.md and add

ops.output(...)

taps in your

<slug>.py

for true tensor diffs inside each block — see layer-by-layer-debugging.md.

Fix the layer, then re-run

Edit

<slug>.py

to fix the identified layer, restart

pixi run max serve

, re-run

compare_layers.py

. When top-1 logprob matches (rel_diff < 5%), logits are aligned at that prompt. For block-local confirmation, use manual

ops.output()

taps.

If you fix a layer and the divergence point doesn't move, you fixed the wrong thing. Revert and re-read the HF source for that layer.

Check against Hugging Face

Run the model end-to-end with pretrained weights, then run HF on the same prompt with greedy sampling. On the MAX side, use the dtype that matches the weight encoding the model supports (most models ship bfloat16). Outputs should be identical or nearly identical; small BF16/FP16 rounding can cause divergence past a dozen tokens. Persistent divergence in the first tokens after the divergence hunt passed usually means tokenizer/chat-template mismatch, dtype mismatch with the released weights, or nonzero MAX sampling.

When matching text comes out, the port is done for greedy text. Real "done" depends on the validation depth picked during planning — pick a tier from 1 (smoke) to 6 (logit parity).

Full HF-comparison recipe, divergence triage, and the 6-tier validation table: validation-tiers.md.

Common pitfalls

Use pitfalls.md as an index — find your symptom, then load the one category file (config, weights, graph, or serving) — honest-docstrings.md for the docstring audit specifically. The two big ones:

Scaffold ≠ port. Do not serve or verify until the graph implements every delta in
```
<slug>.py
```
.
Sed-rename leaves donor docstrings intact. Class names get renamed, but docstrings and comments still describe the donor. Rewrite them and run the audit grep before declaring the implementation done.

Tests and CI

When you add

pytest

tests for the ported model, minimize the number of MAX graph compilations per file. Compile once via a module-scoped fixture and reuse it across

@pytest.mark.parametrize

cases. For files that must compile different graphs, parallelize them with Bazel

shard_count

instead of splitting the file. Full patterns and examples: tests-and-ci.md.

import-model

NPX Install

Tags

SKILL.md Content

Import a model into MAX

Phase 1 — Decide & plan

Read
`config.json`

Read the model card

Propose a plan; accept a veto

Compare with other MAX architectures

Phase 2 — Implement

Scaffold the file layout

Implement the graph

Phase 3 — Verify

Check if it generates coherent text

Layer-by-layer divergence hunt

Read the reference implementation

Compare logits (and HF layer stats)

Fix the layer, then re-run

Check against Hugging Face

Common pitfalls

Tests and CI