Publish pinned blocked Gemma conversion toolkit without weights

README-toolkit.md

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+# System One Gemma scorer on Core ML — conversion blocked at base access
+
+This toolkit pins the real trained [`akash-kamat/system-one-gemma`](https://github.com/akash-kamat/system-one-gemma) scorer at commit `cc75aa8042dec965003210fdba033fee8759735e` and its `google/gemma-3-270m` base at revision `9b0cfec892e2bc2afd938c98eabe4e4a7b1e0ca1`. The 15 MB adapter contains a trained BF16 `score.weight` with shape `[1, 640]`; `modules_to_save` includes `score`. Stock Gemma or a replacement scalar head would not be the tracked model. `assets.lock.json` pins SHA-256 hashes for the adapter, metadata, tokenizer and audited runtime.
+
+**Current status: no Core ML model has been produced or validated.** Google gates the base. The authenticated Hugging Face account on this Mac receives HTTP 403 even for its tiny `config.json`. The exact next step is for the account owner to [review and accept the Gemma terms on the base model page](https://huggingface.co/google/gemma-3-270m), then run `uv run python assets.py` again. Do not use another account's token or an ungated mirror. The source-only [FluidInference/system-one-gemma-coreml](https://huggingface.co/FluidInference/system-one-gemma-coreml) repository contains no Gemma or trained adapter weights.
+
+The upstream web app is the serving reference: it scores each state/question/option string with a Gemma sequence-classification head, right-pads the candidate batch, uses a **256-token** cap, and applies **temperature 2.35** before softmax. It keeps the question/option tail and truncates the end of the state. Its CLI (`infer.py`) defaults to 384 tokens and temperature 1.0 instead; that is a different configuration. This conversion targets the released web-app behavior. It exposes a complete choice probability distribution and chosen option. The upstream scorer has no separate trained Boolean, ordered-score, or abstention head, so this toolkit does not claim those native decision types or the historical Decision Index score of 17.09. The historical tracker checkpoint/adapter pair is unverified.
+
+After legitimate access is granted, on Apple Silicon:
+
+```bash
+uv sync --frozen
+uv run python assets.py
+uv run python convert-coreml.py
+uv run python verify-parity.py
+uv run pytest -q
+```
+
+`convert-coreml.py` is a prepared, **untested** FP16 L256/K16 export. It checks that PEFT's merge retained the exact trained scalar head and writes 16 raw choice logits; host code in `scorer.py` batches options in groups of 16 and applies the upstream temperature/softmax across the full option set. `verify-parity.py` is prepared to compare the native model and Core ML on the eight real upstream demo questions. A produced package must not be published as validated until this check passes and a new report records its size, latency, and compute placement. The prepared export has not been profiled on the Neural Engine.
+
+The upstream repository licenses its code under Apache-2.0 but says its pretrained scorer weights inherit a **noncommercial restriction** from training data. The trained-weight redistribution permission is not sufficiently clear for us to rehost them. Google's [Gemma Terms](https://ai.google.dev/gemma/terms) also govern a converted derivative; section 3.1 specifies downstream restrictions, terms copies, modification notices, and a Notice file. Access to the base and permission to publish the complete converted artifact are separate conditions. This toolkit does not include or redistribute either weight set.

README.md

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+# System One Gemma Core ML conversion toolkit (source only)
+
+**Blocked, no model weights or Core ML package here.** This repo hosts pinned conversion source for [`akash-kamat/system-one-gemma`](https://github.com/akash-kamat/system-one-gemma) at `cc75aa8042dec965003210fdba033fee8759735e` with [`google/gemma-3-270m`](https://huggingface.co/google/gemma-3-270m) at `9b0cfec892e2bc2afd938c98eabe4e4a7b1e0ca1`. The current authenticated account receives HTTP 403 for the gated base. The source's trained adapter contains the scalar score head, but neither the adapter nor any base or derivative weights are redistributed here.
+
+Read [README-toolkit.md](README-toolkit.md) for reproducible commands, exact native semantics, validation gates, and license/access notes. The scripts are prepared and **untested against the complete model** until access is granted. No latency, ANE, size, parity, or Decision Index claim is made.
+
+The upstream scorer README states that the pretrained weights inherit a noncommercial restriction. [Gemma Terms](https://ai.google.dev/gemma/terms) govern the base and converted derivatives. Any future artifact requires both legitimate access and a separate redistribution-rights review.

STATUS.md

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+# System One Gemma status — 2026-09-22
+
+- `blocked_gated_base`: authenticated `hf_hub_download("google/gemma-3-270m", "config.json", revision="9b0cfec892e2bc2afd938c98eabe4e4a7b1e0ca1")` returned `GatedRepoError` HTTP 403.
+- The exact upstream trained adapter, score-head tensor, SHA-256 hashes, serving renderer, 256-token limit, and 2.35 temperature are pinned and source-audited.
+- The Core ML exporter and native-vs-Core ML parity runner are prepared but **have not executed**. There is no validated Core ML package, speed number, ANE placement, or Decision Index score.
+- Trained-weight redistribution needs an explicit rights determination. This public toolkit excludes the adapter and base weights.
+- Next: account owner accepts the [Gemma access terms](https://huggingface.co/google/gemma-3-270m). Then fetch exact base, run conversion and parity, review Gemma and upstream weight terms before publishing any converted weights.

assets.lock.json

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+{
+  "source_repo": "akash-kamat/system-one-gemma",
+  "source_revision": "cc75aa8042dec965003210fdba033fee8759735e",
+  "source_url": "https://github.com/akash-kamat/system-one-gemma",
+  "base_repo": "google/gemma-3-270m",
+  "base_revision": "9b0cfec892e2bc2afd938c98eabe4e4a7b1e0ca1",
+  "base_license": "gemma",
+  "trained_weights_license": "noncommercial restriction stated by upstream README; redistribution permission unresolved",
+  "files": {
+    "pretrained-scorer/adapter_model.safetensors": "1cbbc16e5006a1b8bcd4874a8e135e6f09428fc87005260eaa2ba0d4d724c113",
+    "pretrained-scorer/adapter_config.json": "20bb5a2892f112f7eedb5934d733e1e29623b8f8a45209945e6632e6c1959839",
+    "pretrained-scorer/tokenizer.json": "daab2354f8a74e70d70b4d1f804939b68a8c9624dd06cb7858e52dd8970e9726",
+    "pretrained-scorer/tokenizer_config.json": "689a314d20c6f4406ab4115979926f4791cbc68d191eb0833afe6d1661b5af67",
+    "pretrained-scorer/metrics.json": "6015b9e766c4947f119768080b173626d7abab2210576fb5e0020d4e93ca3f05",
+    "demos.json": "d0553aaab7b777204068679e4f8e913eb24ede6f8778eb6490cb0e4bdfd1018e",
+    "infer.py": "1a98e04a1431862bec1b79969822c8a156d4e0d9a19c81ad4c604733afc26f67",
+    "app.py": "c4f7d71973ec36f852feb6a0440ebf617284c98d42fa88ef93778c2d28d57fd2"
+  },
+  "native_serving": {
+    "reference": "app.py",
+    "max_length": 256,
+    "temperature": 2.35,
+    "trained_score_tensor": "base_model.model.score.weight",
+    "score_shape": [1, 640]
+  },
+  "historical_revision_verified": false
+}

assets.py

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+"""Fetch the pinned upstream scorer and fail clearly when Gemma access is absent."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import urllib.request
+from pathlib import Path
+
+from huggingface_hub import hf_hub_download
+from huggingface_hub.errors import GatedRepoError
+
+ROOT = Path(__file__).resolve().parent
+LOCK = json.loads((ROOT / "assets.lock.json").read_text())
+SOURCE = ROOT / "build" / "source"
+
+
+def sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        for block in iter(lambda: stream.read(8 * 1024 * 1024), b""):
+            digest.update(block)
+    return digest.hexdigest()
+
+
+def check_base_access() -> Path:
+    """Check a tiny config before attempting any model-weight download."""
+    try:
+        return Path(hf_hub_download(LOCK["base_repo"], "config.json", revision=LOCK["base_revision"]))
+    except GatedRepoError as error:
+        raise PermissionError(
+            "Gemma 3 270M access denied. Sign in to Hugging Face, review and accept the terms at "
+            "https://huggingface.co/google/gemma-3-270m, then rerun with that approved account. "
+            "Do not substitute an unrelated Gemma checkpoint."
+        ) from error
+
+
+def fetch_source(*, include_adapter: bool = True) -> Path:
+    """Fetch only the locked public GitHub files, without executing upstream code."""
+    SOURCE.mkdir(parents=True, exist_ok=True)
+    for relative, expected in LOCK["files"].items():
+        if not include_adapter and relative.endswith("adapter_model.safetensors"):
+            continue
+        target = SOURCE / relative
+        if not target.exists():
+            target.parent.mkdir(parents=True, exist_ok=True)
+            url = f"https://raw.githubusercontent.com/akash-kamat/system-one-gemma/{LOCK['source_revision']}/{relative}"
+            with urllib.request.urlopen(url, timeout=60) as response, target.open("wb") as output:
+                for block in iter(lambda: response.read(8 * 1024 * 1024), b""):
+                    output.write(block)
+        if sha256(target) != expected:
+            target.unlink(missing_ok=True)
+            raise ValueError(f"Pinned source hash mismatch: {relative}")
+    return SOURCE
+
+
+def fetch_base() -> Path:
+    """Download the exact licensed base only after account access is confirmed."""
+    check_base_access()
+    root = ROOT / "build" / "base"
+    root.mkdir(parents=True, exist_ok=True)
+    for name in ("config.json", "model.safetensors", "tokenizer.json", "tokenizer_config.json"):
+        cached = Path(hf_hub_download(LOCK["base_repo"], name, revision=LOCK["base_revision"]))
+        target = root / name
+        if not target.exists():
+            target.symlink_to(cached)
+    return root
+
+
+if __name__ == "__main__":
+    check_base_access()
+    print(fetch_source())

convert-coreml.py

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+"""Convert the pinned trained System One scorer after the Gemma gate is accepted."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import time
+from pathlib import Path
+
+from assets import LOCK, ROOT, check_base_access, sha256
+from native_reference import MAX_LENGTH, encode_options, pad_batch
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--output-dir", type=Path, default=ROOT / "build")
+    args = parser.parse_args()
+    check_base_access()  # Fail before importing Torch/Core ML or acquiring hundreds of MB.
+
+    import coremltools as ct
+    import numpy as np
+    import torch
+
+    from export_model import export_wrapper, load_trained_scorer
+
+    torch.set_num_threads(4)
+    tokenizer, native = load_trained_scorer()
+    source = ROOT / "build" / "source"
+    example = json.loads((source / "demos.json").read_text())[0]
+    sequences = encode_options(tokenizer, example["state"], example["question"], example["options"])
+    # The trace uses real upstream demo tokens. Repeat them to fill the fixed K16 graph.
+    sequences = (sequences * 16)[:16]
+    ids, mask = pad_batch(sequences, tokenizer.pad_token_id, MAX_LENGTH)
+    example_tensors = (torch.tensor(ids, dtype=torch.int32), torch.tensor(mask, dtype=torch.int32))
+    wrapper = export_wrapper(native)
+    with torch.inference_mode():
+        original = native(input_ids=example_tensors[0].long(), attention_mask=example_tensors[1].long()).logits
+        wrapped = wrapper(*example_tensors)
+    wrapper_error = float((original - wrapped).abs().max())
+    if wrapper_error > 1e-5:
+        raise ValueError(f"wrapper changed the trained scorer: {wrapper_error}")
+    traced = torch.jit.trace(wrapper, example_tensors)
+
+    started = time.perf_counter()
+    coreml = ct.convert(
+        traced,
+        convert_to="mlprogram",
+        minimum_deployment_target=ct.target.iOS17,
+        compute_precision=ct.precision.FLOAT16,
+        compute_units=ct.ComputeUnit.CPU_ONLY,
+        inputs=[
+            ct.TensorType(name="input_ids", shape=(16, MAX_LENGTH), dtype=np.int32),
+            ct.TensorType(name="attention_mask", shape=(16, MAX_LENGTH), dtype=np.int32),
+        ],
+        outputs=[ct.TensorType(name="logits", dtype=np.float32)],
+    )
+    coreml.short_description = "Trained System One Gemma scalar scorer; choice temperature 2.35 in host"
+    coreml.author = "Akash Kamat (trained weights); Fluid Inference (Core ML conversion)"
+    coreml.license = "Gemma Terms of Use; trained scorer noncommercial restriction"
+    coreml.user_defined_metadata.update(
+        {
+            "source_repo": LOCK["source_repo"],
+            "source_revision": LOCK["source_revision"],
+            "base_repo": LOCK["base_repo"],
+            "base_revision": LOCK["base_revision"],
+            "trained_adapter_sha256": LOCK["files"]["pretrained-scorer/adapter_model.safetensors"],
+            "output_contract": "16 scalar logits; apply temperature 2.35 and softmax to real candidates in host",
+        }
+    )
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+    package = args.output_dir / "system_one_gemma_fp16_L256_K16.mlpackage"
+    coreml.save(str(package))
+    report = {
+        "package": package.name,
+        "source_revision": LOCK["source_revision"],
+        "base_revision": LOCK["base_revision"],
+        "parameters": sum(p.numel() for p in native.parameters()),
+        "trained_score_head_verified": True,
+        "wrapper_max_logit_error": wrapper_error,
+        "conversion_seconds": time.perf_counter() - started,
+        "package_bytes": sum(path.stat().st_size for path in package.rglob("*") if path.is_file()),
+        "package_files_sha256": {
+            str(path.relative_to(package)): sha256(path) for path in package.rglob("*") if path.is_file()
+        },
+        "parity_verified": False,
+    }
+    (args.output_dir / "conversion.json").write_text(json.dumps(report, indent=2) + "\n")
+    print(json.dumps(report, indent=2))
+
+
+if __name__ == "__main__":
+    main()

export_model.py

@@ -0,0 +1,51 @@
+"""Load the exact trained Gemma scorer and expose its scalar logit path."""
+
+from __future__ import annotations
+
+from assets import LOCK, fetch_base, fetch_source
+
+
+def load_trained_scorer():
+    """Return tokenizer and merged scorer, refusing an absent or altered trained head."""
+    # Access check happens before any model allocation or download of large base weights.
+    base = fetch_base()
+    source = fetch_source()
+
+    import torch
+    from peft import PeftModel
+    from safetensors.torch import load_file
+    from transformers import AutoTokenizer, Gemma3TextForSequenceClassification
+
+    tokenizer = AutoTokenizer.from_pretrained(base, local_files_only=True)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    model = Gemma3TextForSequenceClassification.from_pretrained(
+        base, num_labels=1, dtype=torch.float32, local_files_only=True
+    )
+    model.config.pad_token_id = tokenizer.pad_token_id
+    model.config.eos_token_id = tokenizer.eos_token_id
+    peft = PeftModel.from_pretrained(model, source / "pretrained-scorer", local_files_only=True)
+    merged = peft.merge_and_unload().eval()
+    trained = load_file(source / "pretrained-scorer" / "adapter_model.safetensors")
+    trained_head = trained[LOCK["native_serving"]["trained_score_tensor"]].float()
+    actual_head = merged.score.weight.detach().float()
+    if tuple(trained_head.shape) != tuple(LOCK["native_serving"]["score_shape"]):
+        raise ValueError("locked trained head shape changed")
+    if not torch.equal(trained_head, actual_head):
+        raise ValueError("merged model lost the trained scalar score head")
+    return tokenizer, merged
+
+
+def export_wrapper(model):
+    """Make a traceable wrapper that returns only the trained choice logit."""
+    import torch
+
+    class ScalarScorer(torch.nn.Module):
+        def __init__(self, native):
+            super().__init__()
+            self.native = native
+
+        def forward(self, input_ids, attention_mask):
+            return self.native(input_ids=input_ids.long(), attention_mask=attention_mask.long()).logits.float()
+
+    return ScalarScorer(model).eval()

native_reference.py

@@ -0,0 +1,58 @@
+"""The released app.py choice-scorer rendering, padding, and calibration."""
+
+from __future__ import annotations
+
+import math
+from collections.abc import Sequence
+
+MAX_LENGTH = 256
+TEMPERATURE = 2.35
+
+
+def encode(tokenizer, state: str, question: str, option: str, max_length: int = MAX_LENGTH) -> list[int]:
+    """Keep the question/option tail; truncate the state from its end, as upstream does."""
+    tail = tokenizer("\n\nQuestion:\n" + question + "\n\nOption:\n" + option, add_special_tokens=False)["input_ids"]
+    if len(tail) >= max_length:
+        return tail[-max_length:]
+    head = tokenizer("State:\n" + state, add_special_tokens=False)["input_ids"]
+    return head[: max_length - len(tail)] + tail
+
+
+def encode_options(tokenizer, state: str, question: str, options: Sequence[str]) -> list[list[int]]:
+    if len(options) < 2:
+        raise ValueError("the native app requires at least two options")
+    if any(not option.strip() for option in options):
+        raise ValueError("options must be nonempty")
+    return [encode(tokenizer, state, question, option) for option in options]
+
+
+def pad_batch(
+    sequences: Sequence[Sequence[int]], pad_id: int, length: int | None = None
+) -> tuple[list[list[int]], list[list[int]]]:
+    """Right-pad to the longest candidate (or fixed export length), with true token masks."""
+    if not sequences:
+        raise ValueError("empty candidate batch")
+    width = min(max(map(len, sequences)), MAX_LENGTH) if length is None else length
+    if width > MAX_LENGTH or width < max(map(len, sequences)):
+        raise ValueError("invalid padding length")
+    ids = [list(row) + [pad_id] * (width - len(row)) for row in sequences]
+    masks = [[1] * len(row) + [0] * (width - len(row)) for row in sequences]
+    return ids, masks
+
+
+def softmax(logits: Sequence[float], temperature: float = TEMPERATURE) -> list[float]:
+    if not logits or temperature <= 0:
+        raise ValueError("need logits and a positive temperature")
+    scaled = [float(value) / temperature for value in logits]
+    peak = max(scaled)
+    weights = [math.exp(value - peak) for value in scaled]
+    total = sum(weights)
+    return [weight / total for weight in weights]
+
+
+def choose(options: Sequence[str], logits: Sequence[float]) -> dict:
+    if len(options) != len(logits):
+        raise ValueError("one scalar logit is required per option")
+    probabilities = softmax(logits)
+    index = max(range(len(options)), key=probabilities.__getitem__)
+    return {"selected_index": index, "selected_option": options[index], "probabilities": probabilities}

publish-source.py

@@ -0,0 +1,43 @@
+"""Publish the blocked toolkit's allowlisted source files, never third-party weights."""
+
+from __future__ import annotations
+
+from huggingface_hub import CommitOperationAdd, HfApi
+
+from assets import ROOT
+
+REPO = "FluidInference/system-one-gemma-coreml"
+ALLOWLIST = {
+    "README.md": ROOT / "hf" / "README.md",
+    "README-toolkit.md": ROOT / "README.md",
+    "STATUS.md": ROOT / "STATUS.md",
+    "assets.lock.json": ROOT / "assets.lock.json",
+    "assets.py": ROOT / "assets.py",
+    "native_reference.py": ROOT / "native_reference.py",
+    "export_model.py": ROOT / "export_model.py",
+    "convert-coreml.py": ROOT / "convert-coreml.py",
+    "scorer.py": ROOT / "scorer.py",
+    "verify-parity.py": ROOT / "verify-parity.py",
+    "publish-source.py": ROOT / "publish-source.py",
+    "pyproject.toml": ROOT / "pyproject.toml",
+    "uv.lock": ROOT / "uv.lock",
+    "tests/test_source_contract.py": ROOT / "tests" / "test_source_contract.py",
+    "reports/source-audit.json": ROOT / "reports" / "source-audit.json",
+}
+
+
+def main() -> None:
+    operations = [CommitOperationAdd(path_in_repo=remote, path_or_fileobj=path) for remote, path in ALLOWLIST.items()]
+    api = HfApi()
+    api.create_repo(REPO, repo_type="model", private=False, exist_ok=True)
+    result = api.create_commit(
+        repo_id=REPO,
+        repo_type="model",
+        operations=operations,
+        commit_message="Publish pinned blocked Gemma conversion toolkit without weights",
+    )
+    print(f"{REPO} revision {result.oid}")
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -0,0 +1,28 @@
+[project]
+name = "system-one-gemma-coreml"
+version = "0.1.0"
+description = "Pinned trained System One Gemma scorer conversion, gated pending model access"
+requires-python = ">=3.12,<3.13"
+dependencies = [
+    "coremltools==9.0",
+    "huggingface-hub>=0.34",
+    "numpy<2.3",
+    "peft==0.20.0",
+    "safetensors>=0.6",
+    "torch==2.7.0",
+    "transformers==5.17.0",
+]
+
+[dependency-groups]
+dev = ["pytest>=8.4", "ruff>=0.13"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+pythonpath = ["."]
+
+[tool.ruff]
+line-length = 120
+target-version = "py312"
+
+[tool.ruff.lint]
+select = ["E", "F", "I"]

reports/source-audit.json

@@ -0,0 +1,20 @@
+{
+  "audit_date": "2026-09-22",
+  "source_repo": "akash-kamat/system-one-gemma",
+  "source_revision": "cc75aa8042dec965003210fdba033fee8759735e",
+  "base_repo": "google/gemma-3-270m",
+  "base_revision": "9b0cfec892e2bc2afd938c98eabe4e4a7b1e0ca1",
+  "base_access": "GatedRepoError HTTP 403 for authenticated config.json request",
+  "trained_adapter_sha256": "1cbbc16e5006a1b8bcd4874a8e135e6f09428fc87005260eaa2ba0d4d724c113",
+  "trained_head_tensor": "base_model.model.score.weight",
+  "trained_head_shape": [1, 640],
+  "trained_head_dtype": "BF16",
+  "peft_modules_to_save_includes_score": true,
+  "released_web_runtime_max_tokens": 256,
+  "released_web_runtime_temperature": 2.35,
+  "historical_tracker_revision_verified": false,
+  "coreml_converted": false,
+  "coreml_parity_verified": false,
+  "trained_weight_redistribution_permission_resolved": false,
+  "source_contract_unit_tests": "4 passed"
+}

scorer.py

@@ -0,0 +1,39 @@
+"""Run the converted trained scalar scorer over complete option sets."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+
+from assets import ROOT
+from native_reference import MAX_LENGTH, choose, encode_options, pad_batch
+
+
+def score_coreml(model, tokenizer, state: str, question: str, options: list[str]) -> dict:
+    """Pad each independent K16 scorer call, then softmax all real logits jointly."""
+    sequences = encode_options(tokenizer, state, question, options)
+    logits = []
+    for offset in range(0, len(sequences), 16):
+        chunk = sequences[offset : offset + 16]
+        padded = chunk + [chunk[0]] * (16 - len(chunk))
+        ids, masks = pad_batch(padded, tokenizer.pad_token_id, MAX_LENGTH)
+        result = model.predict(
+            {"input_ids": np.asarray(ids, dtype=np.int32), "attention_mask": np.asarray(masks, dtype=np.int32)}
+        )
+        logits.extend(np.asarray(result["logits"]).reshape(-1)[: len(chunk)].astype(float).tolist())
+    return {**choose(options, logits), "logits": logits}
+
+
+def load_runtime(package: Path = ROOT / "build" / "system_one_gemma_fp16_L256_K16.mlpackage"):
+    """Load only the package and public base tokenizer after terms acceptance."""
+    import coremltools as ct
+    from transformers import AutoTokenizer
+
+    from assets import fetch_base
+
+    base = fetch_base()
+    tokenizer = AutoTokenizer.from_pretrained(base, local_files_only=True)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    return ct.models.MLModel(str(package), compute_units=ct.ComputeUnit.ALL), tokenizer

tests/test_source_contract.py

@@ -0,0 +1,54 @@
+"""Small deterministic checks for the audited source contract, without loading Gemma."""
+
+from __future__ import annotations
+
+import json
+import struct
+
+import pytest
+
+from assets import LOCK, fetch_source, sha256
+from native_reference import TEMPERATURE, choose, encode, pad_batch, softmax
+
+
+def test_real_trained_adapter_contains_saved_scalar_head() -> None:
+    source = fetch_source()
+    adapter = source / "pretrained-scorer" / "adapter_model.safetensors"
+    with adapter.open("rb") as stream:
+        header_length = struct.unpack("<Q", stream.read(8))[0]
+        header = json.loads(stream.read(header_length))
+    tensor = header[LOCK["native_serving"]["trained_score_tensor"]]
+    assert tensor["shape"] == [1, 640]
+    assert tensor["dtype"] == "BF16"
+    assert sha256(adapter) == LOCK["files"]["pretrained-scorer/adapter_model.safetensors"]
+
+
+def test_locked_metrics_match_released_app_calibration() -> None:
+    source = fetch_source(include_adapter=False)
+    metrics = json.loads((source / "pretrained-scorer" / "metrics.json").read_text())
+    assert metrics["temperature"] == TEMPERATURE
+    assert metrics["max_len"] == LOCK["native_serving"]["max_length"] == 256
+    adapter_config = json.loads((source / "pretrained-scorer" / "adapter_config.json").read_text())
+    assert "score" in adapter_config["modules_to_save"]
+    assert adapter_config["base_model_name_or_path"] == LOCK["base_repo"]
+
+
+def test_native_rendering_preserves_question_tail_and_right_padding() -> None:
+    class CharacterTokenizer:
+        def __call__(self, text: str, add_special_tokens: bool):
+            assert not add_special_tokens
+            return {"input_ids": [ord(character) for character in text]}
+
+    tokens = encode(CharacterTokenizer(), "state", "question", "option", max_length=28)
+    assert tokens[-len("\n\nOption:\noption") :] == [ord(c) for c in "\n\nOption:\noption"]
+    ids, mask = pad_batch([[1, 2, 3], [4]], 0, 4)
+    assert ids == [[1, 2, 3, 0], [4, 0, 0, 0]]
+    assert mask == [[1, 1, 1, 0], [1, 0, 0, 0]]
+
+
+def test_calibrated_choice_and_invalid_temperature() -> None:
+    result = choose(["first", "second"], [0.0, 2.35])
+    assert result["selected_index"] == 1
+    assert result["probabilities"] == pytest.approx([0.2689414214, 0.7310585786])
+    with pytest.raises(ValueError):
+        softmax([1.0], temperature=0.0)

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verify-parity.py

@@ -0,0 +1,86 @@
+"""Check native scorer versus fixed K16 Core ML over real upstream demo requests."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import statistics
+import time
+from pathlib import Path
+
+import numpy as np
+
+from assets import ROOT, check_base_access
+from native_reference import choose, encode_options, pad_batch
+from scorer import score_coreml
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--package", type=Path, default=ROOT / "build" / "system_one_gemma_fp16_L256_K16.mlpackage")
+    parser.add_argument("--repeats", type=int, default=10)
+    args = parser.parse_args()
+    check_base_access()
+
+    import coremltools as ct
+    import torch
+
+    from export_model import load_trained_scorer
+
+    tokenizer, native = load_trained_scorer()
+    coreml = ct.models.MLModel(str(args.package), compute_units=ct.ComputeUnit.ALL)
+    demos = json.loads((ROOT / "build" / "source" / "demos.json").read_text())
+    rows = []
+    for demo in demos:
+        sequences = encode_options(tokenizer, demo["state"], demo["question"], demo["options"])
+        ids, masks = pad_batch(sequences, tokenizer.pad_token_id)
+        with torch.inference_mode():
+            native_logits = (
+                native(
+                    input_ids=torch.tensor(ids, dtype=torch.long),
+                    attention_mask=torch.tensor(masks, dtype=torch.long),
+                )
+                .logits.reshape(-1)
+                .float()
+                .tolist()
+            )
+        native_choice = choose(demo["options"], native_logits)
+        times = []
+        converted = None
+        for _ in range(args.repeats):
+            start = time.perf_counter()
+            converted = score_coreml(coreml, tokenizer, demo["state"], demo["question"], demo["options"])
+            times.append((time.perf_counter() - start) * 1000)
+        assert converted is not None
+        error = max(abs(a - b) for a, b in zip(native_choice["probabilities"], converted["probabilities"]))
+        rows.append(
+            {
+                "demo": demo["name"],
+                "candidates": len(demo["options"]),
+                "native_selected_index": native_choice["selected_index"],
+                "coreml_selected_index": converted["selected_index"],
+                "max_calibrated_probability_error": error,
+                "median_end_to_end_ms": statistics.median(times),
+            }
+        )
+    report = {
+        "package": args.package.name,
+        "source": "pinned upstream demos.json",
+        "questions": len(rows),
+        "selected_id_agreements": sum(r["native_selected_index"] == r["coreml_selected_index"] for r in rows),
+        "max_calibrated_probability_error": float(np.max([r["max_calibrated_probability_error"] for r in rows])),
+        "rows": rows,
+    }
+    report["passed"] = (
+        report["selected_id_agreements"] == len(rows) and report["max_calibrated_probability_error"] <= 0.02
+    )
+    target = ROOT / "reports" / "verification-L256-K16.json"
+    target.parent.mkdir(parents=True, exist_ok=True)
+    target.write_text(json.dumps(report, indent=2) + "\n")
+    print(json.dumps(report, indent=2))
+    if not report["passed"]:
+        raise SystemExit(1)
+
+
+if __name__ == "__main__":
+    main()