Imported from shakfu/mdsp (.claude/skills/import-model/SKILL.md). Install upstream with npx skills add shakfu/mdsp --skill import-model. Copyright stays with the author.

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 or max on the shell PATH):

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:

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 in __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>) to 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:

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:

pixi run python inspect_hf.py <HF_MODEL_ID>

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

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 in 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:

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):

pixi run python list_native_archs.py

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

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:

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.

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 carrying the donor's memory_planner= verbatim, including a configured planner such as PagedMemoryPlanner.with_activation_reservation(...) (verify name=, encoding, and memory_planner=). When the donor declares no planner, the field is left out with a TODO instead of being defaulted
• model_config.py — donor config (must be rewired during implementation)
• model.py — pipeline model shell (inherits the donor's batch processor, so input preparation keeps working without a batch_processor.py)
• 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:

1. model_config.py — wire every config.json key from Phase 1 / inspect_hf.py. Set construct_kv_params() head counts and head_dim to match HF.
2. 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).
3. <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)
4. arch.py — confirm name= matches architectures[0]; default_encoding matches Hub torch_dtype. Keep the scaffolded memory_planner= (copied from the donor); without it MAX budgets no activation memory for the KV cache and skips max_batch_size inference. If scaffold left a memory_planner TODO, the donor had none — resolve it before serving: KV-cache ports need memory_planner=PagedMemoryPlanner, and only architectures doing their own memory estimation (diffusion, embedding) should leave it unset. Add batching= only if the port's batching diverges from the donor's — the model shell inherits the donor's batch processor.
5. 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):

pixi run rg -i -n 'qwen|llama|mistral|cohere|gemma|phi|deepseek|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):

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.

Parity/coherence failure (invoke debug-model)

When the server starts but output is wrong (gibberish, wrong greedy token at index K, high logit cosine with wrong argmax, or coherent for N tokens then diverges), stop the scalar-tap loop and load the debug-model skill.

That skill is the authoritative workflow for silent corruption. It mandates:

1. HF sanity-check on the same prompt + checkpoint
2. Per-layer HF vs MAX tensor-dump comparators (not ops.print eyeballing)
3. Parallel investigation agents with numerical verification before recompile
4. Serve-vs-pipeline bisect when dumps match but generated text diverges

Use import-model for bring-up scaffolding and gates. Use debug-model for the divergence hunt itself.

Quick logit probe (first 5 minutes only)

Before building dumpers, a fast sanity check:

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. This script prints HF-only layer stats and compares top-1 logprob at the prompt. See layer-by-layer-debugging.md for flag details.

If logprobs diverge or output is garbage, switch to debug-model. Do not iterate with manual ops.output() taps alone. The symptom catalog in divergences.md still applies once the comparator localizes the failing 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.