Release QAdapt v1 with paired Ising-fast baseline

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+Ising-Decoding
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+ONNX Runtime - MIT License
+<https://github.com/microsoft/onnxruntime>
+
+License at <https://github.com/microsoft/onnxruntime/blob/main/LICENSE>
+Copyright (c) Microsoft Corporation
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+----------------------------------------------------------------
+
+NVIDIA ModelOpt - Apache 2.0
+<https://github.com/NVIDIA/TensorRT-Model-Optimizer>
+
+License at <https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/LICENSE>
+Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+
+----------------------------------------------------------------
+
+TensorBoard - Apache 2.0
+<https://github.com/tensorflow/tensorboard>
+
+License at <https://github.com/tensorflow/tensorboard/blob/master/LICENSE>
+Copyright 2015 The TensorFlow Authors
+
+----------------------------------------------------------------
+
+torchinfo - MIT License
+<https://github.com/TylerYep/torchinfo>
+
+License at <https://github.com/TylerYep/torchinfo/blob/main/LICENSE>
+Copyright (c) 2020 Tyler Yep
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+----------------------------------------------------------------
+
+Matplotlib - BSD-compatible (PSF-based)
+<https://github.com/matplotlib/matplotlib>
+
+License at <https://github.com/matplotlib/matplotlib/blob/main/LICENSE/LICENSE>
+Copyright (c) 2012-2013 Matplotlib Development Team; All Rights Reserved
+
+----------------------------------------------------------------
+
+CuPy - MIT License
+<https://github.com/cupy/cupy>
+
+License at <https://github.com/cupy/cupy/blob/main/LICENSE>
+Copyright (c) 2015 Preferred Infrastructure, Inc.
+Copyright (c) 2015 Preferred Networks, Inc.
+
+----------------------------------------------------------------
+
+TensorRT - Apache 2.0
+<https://github.com/NVIDIA/TensorRT>
+
+License at <https://github.com/NVIDIA/TensorRT/blob/main/LICENSE>
+Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
+
+----------------------------------------------------------------
+
+onnxscript - MIT License
+<https://github.com/microsoft/onnxscript>
+
+License at <https://github.com/microsoft/onnxscript/blob/main/LICENSE>
+Copyright (c) Microsoft Corporation
+
+----------------------------------------------------------------
+
+SciPy - BSD-3-Clause
+<https://pypi.org/project/scipy>
+
+License at <https://github.com/scipy/scipy/blob/main/LICENSE.txt>
+Copyright (c) 2001-2002 Enthought, Inc. 2003, SciPy Developers.
+All rights reserved.
+
+----------------------------------------------------------------
+
+ldpc - MIT License
+<https://pypi.org/project/ldpc>
+
+License at <https://github.com/quantumgizmos/ldpc/blob/main/LICENSE>
+Copyright (c) 2024 Joschka Roffe
+
+----------------------------------------------------------------
+
+BeliefMatching - Apache 2.0
+<https://github.com/oscarhiggott/BeliefMatching>
+
+License at <https://github.com/oscarhiggott/BeliefMatching/blob/main/LICENSE>
+Copyright (c) Oscar Higgott
+
+----------------------------------------------------------------
+
+Chromobius - Apache 2.0
+<https://github.com/quantumlib/chromobius>
+
+License at <https://github.com/quantumlib/chromobius/blob/main/LICENSE>
+Copyright 2023 Google LLC
+
+----------------------------------------------------------------

README.md

@@ -0,0 +1,237 @@
+---
+license: apache-2.0
+library_name: pytorch
+tags:
+- quantum-error-correction
+- surface-code
+- neural-decoder
+- pre-decoding
+- continual-learning
+---
+
+# QAdapt v1
+
+This repository contains the final QAdapt checkpoint and the exact
+Ising-fast T0 e100 baseline used for paired evaluation in *QAdapt: Continual
+Syndrome-Adaptive Neural Pre-Decoding for High-Noise Surface-Code Quantum
+Error Correction*.
+
+Release version: `v1`.
+
+The arXiv link will be added after the paper is public. This is a research
+checkpoint bundle, not a Transformers `AutoModel` repository. Use it with the
+pinned NVIDIA/Ising-Decoding revision and the patch supplied here.
+
+## Included models
+
+| Role | File | Model ID | Architecture | Parameters | RF |
+|---|---|---:|---|---:|---:|
+| QAdapt | `model.safetensors` | 111 | HTNet | 650,374 | 9 |
+| Paired baseline | `baselines/ising-fast-t0-e100/model.safetensors` | 1 | `PreDecoderModelMemory_v1` | 912,772 | 9 |
+
+QAdapt is the primary artifact. The bundled Ising-fast checkpoint was trained
+only on T0 for 100 epochs and is included so that all reported paired
+comparisons can be evaluated from one repository. Its exact architecture
+metadata is stored in `baselines/ising-fast-t0-e100/config.json`.
+
+## Technical overview
+
+```text
+detector events [B, 4, T, H, W]
+        -> neural local pre-decoder
+        -> predicted local correction and residual syndrome
+        -> PyMatching global residual decoder
+        -> logical prediction
+```
+
+![QAdapt workflow from the paper](assets/figure2_qadapt_pipeline.png)
+
+*QAdapt workflow: hardware-informed noise modeling, heterogeneous
+spatiotemporal feature extraction, continual adaptation, and hybrid
+neural--matching inference.*
+
+QAdapt uses a 3-D convolutional stem followed by three HTNet spatiotemporal
+fusion blocks. Each block combines a spatial branch, a temporal branch, and a
+grouped joint 3-D branch using input-adaptive fusion, then applies channel,
+temporal-axis, and spatial-axis gating with a residual connection. The
+input-conditioned head produces four output channels.
+
+The released HTNet uses 112 hidden channels, 168 expanded channels, six joint
+convolution groups, eight normalization groups, and an effective receptive
+field of nine. Full machine-readable parameters are in `config.json`.
+
+The Ising-fast baseline is a dense four-layer 3-D convolutional pre-decoder
+with filters `[128, 128, 128, 4]` and 3x3x3 kernels.
+
+![HTNet architecture from the paper](assets/figure3_htnet_architecture.png)
+
+*HTNet architecture: a 112-channel 3-D stem, three heterogeneous
+spatiotemporal fusion blocks, raw-evidence concatenation, and a four-channel
+correction head.*
+
+## Training scope
+
+Training samples were generated on demand with Stim from the public
+25-parameter circuit-level Pauli configurations under `configs/`.
+
+- QAdapt: T0 -> T1 -> T2 -> T3 -> T4, 20 epochs per task, 100 epochs total.
+- QAdapt training hardware: 4 × NVIDIA A100 GPUs.
+- Continual adaptation: Q-EWC coefficient 100 from T1 onward, with 65,536
+  samples per Fisher estimate.
+- Ising-fast baseline: T0 only, 100 epochs.
+- Willow: zero-shot evaluation only; no training, fine-tuning, calibration, or
+  model selection used Willow samples.
+
+Training orchestration, optimizer state, Fisher state, intermediate
+checkpoints, and logs are intentionally not distributed.
+
+## Install
+
+```bash
+git clone https://github.com/NVIDIA/Ising-Decoding.git
+cd Ising-Decoding
+git checkout 33acb152e403bc189f2effdb07f1a87b34c745f1
+git apply /path/to/QAdapt/qadapt-minimal.patch
+pip install -r code/requirements_public_inference.txt
+```
+
+The patch adds QAdapt model ID 111, SafeTensors loading, the exact public
+configs, and the three inference entry points. It contains no training code or
+intermediate models.
+
+## T0--T4 inference
+
+Run QAdapt alone on T0:
+
+```bash
+PYTHONPATH=code python code/examples/infer_t0_t4.py \
+  --tasks T0 --distances 9 \
+  --model qadapt:111:/path/to/QAdapt/model.safetensors
+```
+
+Run the paired release evaluation:
+
+```bash
+PYTHONPATH=code python code/examples/infer_t0_t4.py \
+  --model qadapt:111:/path/to/QAdapt/model.safetensors \
+  --model ising-fast:1:/path/to/QAdapt/baselines/ising-fast-t0-e100/model.safetensors
+```
+
+The default paired command evaluates T0--T4 at d=9/r=9 with 262,144 shots per
+basis per task and seed 12345. Use `--dry-run` to inspect all five jobs first.
+
+## Synthetic OOD inference
+
+```bash
+PYTHONPATH=code python code/examples/infer_ood.py \
+  --model qadapt:111:/path/to/QAdapt/model.safetensors \
+  --model ising-fast:1:/path/to/QAdapt/baselines/ising-fast-t0-e100/model.safetensors
+```
+
+Defaults run the retained paper grid: 11 axis combinations, multipliers
+1.2/1.5/2.0/2.5/3.0, d=7 and d=9, for 110 jobs total.
+
+## Willow zero-shot inference
+
+The Willow archive is third-party data and is not redistributed here.
+
+```bash
+PYTHONPATH=code python code/scripts/download_google_qec_benchmark.py --extract
+PYTHONPATH=code python code/examples/infer_willow.py \
+  --model qadapt:111:/path/to/QAdapt/model.safetensors \
+  --model ising-fast:1:/path/to/QAdapt/baselines/ising-fast-t0-e100/model.safetensors
+```
+
+Defaults are d=5/d=7, ten rounds, and all available shots: 400,000 at d=5 and
+100,000 at d=7, without fine-tuning.
+
+## Results
+
+### T0--T4 terminal model results
+
+The final e100 checkpoints were evaluated at d=9/r=9 with 262,144 shots per
+logical basis per task and seed 12345.
+
+| Task | PyMatching LER | Ising-fast LER | QAdapt LER |
+|---|---:|---:|---:|
+| T0 | 0.04503 | 0.04094 | **0.03612** |
+| T1 | 0.05489 | 0.05207 | **0.04619** |
+| T2 | 0.15404 | 0.14017 | **0.13012** |
+| T3 | 0.04997 | 0.04532 | **0.04053** |
+| T4 | 0.09811 | 0.09135 | **0.08282** |
+| Mean | 0.08041 | 0.07397 | **0.06716** |
+
+QAdapt lowers mean LER by 9.22% relative to the Ising-fast T0 e100 baseline.
+
+### Synthetic OOD
+
+Each distance aggregates 55 configurations and logical X/Z bases. QAdapt wins
+all 110 configuration-level LER comparisons.
+
+| Distance | Ising-fast LER | QAdapt LER | LER reduction | Ising-fast latency | QAdapt latency |
+|---|---:|---:|---:|---:|---:|
+| d=7 | 0.23447 | **0.22701** | 3.18% | 2.329 | **2.195** |
+| d=9 | 0.24444 | **0.23653** | 3.23% | 4.884 | **4.608** |
+
+![Synthetic OOD results from the paper](assets/figure5_synthetic_ood.png)
+
+*Synthetic OOD results over the five retained noise multipliers. Latency is in
+microseconds per round.*
+
+### Willow zero-shot transfer
+
+| Setting | Metric | Ising-fast | QAdapt | Reduction |
+|---|---|---:|---:|---:|
+| d=5/r=10 | LER | 0.09963 | **0.09386** | 5.79% |
+| d=5/r=10 | Backend latency | 0.704 | **0.694** | 1.43% |
+| d=7/r=10 | LER | 0.08412 | **0.08201** | 2.51% |
+| d=7/r=10 | Backend latency | 1.405 | **1.274** | 9.32% |
+
+![Willow results from the paper](assets/figure6_willow_results.png)
+
+*Zero-shot transfer to Willow at ten rounds. Latency is in microseconds per
+round.*
+
+Full-precision values and protocol metadata are provided in `evaluation.json`.
+The paper's mapped-T0 architecture table used Ising-fast e53 and a T0-only
+HTNet e89; those separate ablation values are not attributed to the final e100
+artifacts distributed here.
+
+Backend latency measures only residual PyMatching decoding. It excludes neural
+inference, device/host transfer, and residual construction and will vary by
+hardware and software environment.
+
+## Integrity
+
+Run from this downloaded model repository:
+
+```bash
+sha256sum -c SHA256SUMS
+```
+
+The two SafeTensors artifacts were compared tensor-by-tensor with their final
+source checkpoints. All tensors match exactly.
+
+## Limitations
+
+These checkpoints target rotated surface-code memory experiments with the
+input layout and noise semantics implemented by the pinned repository. They
+are not standalone end-to-end fault-tolerant systems and have not been
+validated for arbitrary code families, detector layouts, or hardware control
+stacks.
+
+## License and attribution
+
+Apache-2.0. Retain `LICENSE`, `NOTICE`, and the modification notices when
+redistributing the code or patch. Google Willow data remains under its own
+upstream terms and is downloaded separately.
+
+## Citation
+
+```bibtex
+@article{miao2026qadapt,
+  title={QAdapt: Continual Syndrome-Adaptive Neural Pre-Decoding for High-Noise Surface-Code Quantum Error Correction},
+  author={Miao, Ran and Shan, Xiaohan and Luo, Rui and Sun, Xiaoming and Zhang, Jialin and Wei, Yuan and Wan, Mingda},
+  year={2026}
+}
+```

REPRODUCTION_VALIDATION.json

@@ -0,0 +1,180 @@
+{
+  "schema_version": 1,
+  "release_version": "v1",
+  "validated_at": "2026-07-29",
+  "status": "pass",
+  "upstream": {
+    "repository": "https://github.com/NVIDIA/Ising-Decoding.git",
+    "commit": "33acb152e403bc189f2effdb07f1a87b34c745f1",
+    "patch_sha256": "15f8e7d2f18158a769a5ba06d183239090c88c606519c8468f9a814998c6a986",
+    "git_apply_check": "pass",
+    "clean_clone_gpu_smoke": "pass"
+  },
+  "artifacts": {
+    "QAdapt": {
+      "relative_path": "model.safetensors",
+      "safetensors_sha256": "65f979c9f23f1b13b76876c6e5df204d1bd28b0e3322483d966db0efb840600d",
+      "source_checkpoint_sha256": "59d55a948a1a99458f7ccb0bba83335a779ef90eb8b36125407b65d45d771f9f",
+      "tensor_equality": "exact",
+      "tensor_count": 94,
+      "parameter_count": 650374
+    },
+    "Ising-Fast-T0-e100": {
+      "relative_path": "baselines/ising-fast-t0-e100/model.safetensors",
+      "safetensors_sha256": "af5e13e389358d4aa19afa92f6d1ff368e8f64e8497f23c51ddeed9d367f724a",
+      "source_checkpoint_sha256": "c23c9b1507cf4d213ce1b6526f87ee9e2f9db2bd8dc336c16f915b77724a0661",
+      "tensor_equality": "exact",
+      "tensor_count": 8,
+      "parameter_count": 912772
+    }
+  },
+  "environment": {
+    "python": "3.12.3",
+    "torch": "2.7.0a0+79aa17489c.nv25.04",
+    "numpy": "1.26.4",
+    "stim": "1.16.0",
+    "pymatching": "2.4.0",
+    "safetensors": "0.5.3",
+    "cuda": "12.9",
+    "gpu": "NVIDIA A100-SXM4-80GB"
+  },
+  "t0_t4": {
+    "status": "pass",
+    "protocol": {
+      "distance": 9,
+      "rounds": 9,
+      "shots_per_basis_per_task": 262144,
+      "bases": ["X", "Z"],
+      "seed": 12345
+    },
+    "actual_mean_ler_by_task": {
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+        "pymatching": 0.045360565185546875,
+        "ising-fast": 0.04070091247558594,
+        "qadapt": 0.036182403564453125
+      },
+      "T1": {
+        "pymatching": 0.05428314208984375,
+        "ising-fast": 0.05145454406738281,
+        "qadapt": 0.046100616455078125
+      },
+      "T2": {
+        "pymatching": 0.15405654907226562,
+        "ising-fast": 0.14002418518066406,
+        "qadapt": 0.1307964324951172
+      },
+      "T3": {
+        "pymatching": 0.0500640869140625,
+        "ising-fast": 0.04494285583496094,
+        "qadapt": 0.040164947509765625
+      },
+      "T4": {
+        "pymatching": 0.098663330078125,
+        "ising-fast": 0.09106636047363281,
+        "qadapt": 0.0831298828125
+      }
+    },
+    "actual_mean_ler_across_tasks": {
+      "pymatching": 0.08048553466796875,
+      "ising-fast": 0.07363777160644532,
+      "qadapt": 0.06727485656738282
+    },
+    "qadapt_reduction_vs_ising_fast_percent": 8.64083051435735,
+    "max_absolute_ler_delta_vs_release_reference": 0.000681,
+    "interpretation": "All five tasks reproduce within ordinary finite-shot and software-version variation. The paper architecture table used different T0-only epochs and is not evidence for these final e100 artifacts."
+  },
+  "synthetic_ood": {
+    "status": "pass",
+    "protocol": {
+      "axis_combinations": 11,
+      "multipliers": [1.2, 1.5, 2.0, 2.5, 3.0],
+      "distances": [7, 9],
+      "jobs": 110,
+      "shots_per_basis_per_job": 262144,
+      "bases": ["X", "Z"]
+    },
+    "actual": {
+      "d7": {
+        "pymatching_mean_ler": 0.2453592127019709,
+        "ising_fast_mean_ler": 0.23453282442959872,
+        "qadapt_mean_ler": 0.2268583124334162,
+        "qadapt_reduction_vs_ising_fast_percent": 3.2722549668036893,
+        "qadapt_wins": 55,
+        "comparisons": 55,
+        "ising_fast_backend_latency_us_per_round": 2.446139354042408,
+        "qadapt_backend_latency_us_per_round": 2.300856614889897
+      },
+      "d9": {
+        "pymatching_mean_ler": 0.2519276358864524,
+        "ising_fast_mean_ler": 0.24441146850585938,
+        "qadapt_mean_ler": 0.23654133189808238,
+        "qadapt_reduction_vs_ising_fast_percent": 3.2200357274103615,
+        "qadapt_wins": 55,
+        "comparisons": 55,
+        "ising_fast_backend_latency_us_per_round": 5.014288594494714,
+        "qadapt_backend_latency_us_per_round": 4.730247553071063
+      }
+    },
+    "absolute_ler_delta_vs_paper": {
+      "d7_ising_fast": 0.00006020285866476,
+      "d7_qadapt": -0.00014780217950994,
+      "d9_ising_fast": -0.00002417130903765,
+      "d9_qadapt": 0.00000766407359728
+    },
+    "interpretation": "QAdapt wins all 110 retained comparisons. Backend timing is machine-dependent and excludes neural inference and transfers."
+  },
+  "willow": {
+    "status": "pass",
+    "protocol": {
+      "rounds": 10,
+      "bases": ["X", "Z"],
+      "fine_tuning": false,
+      "d5_shots": 400000,
+      "d7_shots": 100000
+    },
+    "actual": {
+      "d5": {
+        "ising_fast_logical_errors": 39852,
+        "ising_fast_ler": 0.09963,
+        "qadapt_logical_errors": 37543,
+        "qadapt_ler": 0.0938575,
+        "qadapt_reduction_vs_ising_fast_percent": 5.79393756900532,
+        "ising_fast_backend_latency_us_per_round": 0.7209028746001422,
+        "qadapt_backend_latency_us_per_round": 0.6945591699331999
+      },
+      "d7": {
+        "ising_fast_logical_errors": 8412,
+        "ising_fast_ler": 0.08412,
+        "qadapt_logical_errors": 8201,
+        "qadapt_ler": 0.08201,
+        "qadapt_reduction_vs_ising_fast_percent": 2.50832144555397,
+        "ising_fast_backend_latency_us_per_round": 1.344656078144908,
+        "qadapt_backend_latency_us_per_round": 1.3134985323995352
+      }
+    },
+    "ler_and_logical_error_match_to_paper": "exact",
+    "interpretation": "The third-party archive is not redistributed. Timing is machine-dependent; the LER and logical-error counts exactly reproduce the report."
+  },
+  "tests": {
+    "public_config": {"tests": 36, "status": "pass"},
+    "safetensors_export_and_load": {"tests": 9, "status": "pass"},
+    "compileall": "pass",
+    "full_upstream_suite": {
+      "tests": 456,
+      "successful": 424,
+      "skipped": 15,
+      "errors": 16,
+      "failures": 1,
+      "status": "environment_incomplete",
+      "non_release_blockers": {
+        "missing_optional_cuquantum": 11,
+        "missing_upstream_nvidia_model_files": 4,
+        "missing_optional_onnx_graphsurgeon": 2
+      }
+    },
+    "python_compat_script": {
+      "status": "environment_constraint_conflict",
+      "detail": "The script pinned the container's NVIDIA pre-release torch build while the public requirements requested the normal torch package, so pip stopped before code checks."
+    }
+  }
+}

SHA256SUMS

@@ -0,0 +1,8 @@
+65f979c9f23f1b13b76876c6e5df204d1bd28b0e3322483d966db0efb840600d  model.safetensors
+af5e13e389358d4aa19afa92f6d1ff368e8f64e8497f23c51ddeed9d367f724a  baselines/ising-fast-t0-e100/model.safetensors
+15f8e7d2f18158a769a5ba06d183239090c88c606519c8468f9a814998c6a986  qadapt-minimal.patch
+6cf4c63502ef8a512859ae48c11a3ea77db997f5874dbc7662ce0d99e7386b30  REPRODUCTION_VALIDATION.json
+d1f64fbb4c8c2af4837a71d93e6e1ee198914234c7e7a4ffdcba8b856d0494ff  assets/figure2_qadapt_pipeline.png
+b099b361bf5d1a1af57d2a7bda199c6987252735b3747267ac6e698372ec1c63  assets/figure3_htnet_architecture.png
+7c8d6e178b8829493a0f2ed4f6bf31a0f97294158ffa939d23deefa7eb407052  assets/figure5_synthetic_ood.png
+6ae49631e848ad7c3dd85544291840d67847ce90f9021f8b4457a2b373e67a06  assets/figure6_willow_results.png

baselines/ising-fast-t0-e100/config.json

@@ -0,0 +1,16 @@
+{
+  "architectures": ["PreDecoderModelMemory_v1"],
+  "model_type": "ising_fast_surface_predecoder",
+  "release_version": "v1",
+  "model_id": 1,
+  "dtype": "float32",
+  "parameters": 912772,
+  "receptive_field": 9,
+  "num_filters": [128, 128, 128, 4],
+  "kernel_size": [3, 3, 3, 3],
+  "code_rotation": "XV",
+  "upstream_commit": "33acb152e403bc189f2effdb07f1a87b34c745f1",
+  "training_tasks": ["T0"],
+  "total_epochs": 100,
+  "willow_used_for_training": false
+}

baselines/ising-fast-t0-e100/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:af5e13e389358d4aa19afa92f6d1ff368e8f64e8497f23c51ddeed9d367f724a
+size 3651904

config.json

@@ -0,0 +1,27 @@
+{
+  "architectures": ["HTnet"],
+  "model_type": "qadapt_htnet",
+  "release_version": "v1",
+  "model_id": 111,
+  "dtype": "float32",
+  "parameters": 650374,
+  "receptive_field": 9,
+  "channels": 112,
+  "expand_channels": 168,
+  "num_blocks": 3,
+  "joint_groups": 6,
+  "norm_groups": 8,
+  "se_reduction": 4,
+  "code_rotation": "XV",
+  "upstream_commit": "33acb152e403bc189f2effdb07f1a87b34c745f1",
+  "training_tasks": ["T0", "T1", "T2", "T3", "T4"],
+  "epochs_per_task": 20,
+  "total_epochs": 100,
+  "training_hardware": {
+    "accelerator": "NVIDIA A100",
+    "accelerator_count": 4
+  },
+  "qewc_lambda": 100,
+  "fisher_samples_per_task": 65536,
+  "willow_used_for_training": false
+}

configs/config_qadapt_t0_base.yaml

@@ -0,0 +1,40 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# QAdapt T0 inference environment.
+
+model_id: 111
+distance: 9
+n_rounds: 9
+
+workflow:
+  task: inference
+
+data:
+  code_rotation: O1
+  noise_model:
+    p_prep_X: 0.0010000
+    p_prep_Z: 0.0010000
+    p_meas_X: 0.0100000
+    p_meas_Z: 0.0100000
+    p_idle_cnot_X: 0.0003330
+    p_idle_cnot_Y: 0.0003330
+    p_idle_cnot_Z: 0.0003330
+    p_idle_spam_X: 0.0006670
+    p_idle_spam_Y: 0.0006670
+    p_idle_spam_Z: 0.0006670
+    p_cnot_IX: 0.0006670
+    p_cnot_IY: 0.0006670
+    p_cnot_IZ: 0.0006670
+    p_cnot_XI: 0.0006670
+    p_cnot_XX: 0.0006670
+    p_cnot_XY: 0.0006670
+    p_cnot_XZ: 0.0006670
+    p_cnot_YI: 0.0006670
+    p_cnot_YX: 0.0006670
+    p_cnot_YY: 0.0006670
+    p_cnot_YZ: 0.0006670
+    p_cnot_ZI: 0.0006670
+    p_cnot_ZX: 0.0006670
+    p_cnot_ZY: 0.0006670
+    p_cnot_ZZ: 0.0006670

configs/config_qadapt_t1_meas_1p5.yaml

@@ -0,0 +1,40 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# Shared QAdapt T1 measurement-noise task.
+
+model_id: 111
+distance: 9
+n_rounds: 9
+
+workflow:
+  task: inference
+
+data:
+  code_rotation: O1
+  noise_model:
+    p_prep_X: 0.0010000
+    p_prep_Z: 0.0010000
+    p_meas_X: 0.0150000
+    p_meas_Z: 0.0150000
+    p_idle_cnot_X: 0.0003330
+    p_idle_cnot_Y: 0.0003330
+    p_idle_cnot_Z: 0.0003330
+    p_idle_spam_X: 0.0006670
+    p_idle_spam_Y: 0.0006670
+    p_idle_spam_Z: 0.0006670
+    p_cnot_IX: 0.0006670
+    p_cnot_IY: 0.0006670
+    p_cnot_IZ: 0.0006670
+    p_cnot_XI: 0.0006670
+    p_cnot_XX: 0.0006670
+    p_cnot_XY: 0.0006670
+    p_cnot_XZ: 0.0006670
+    p_cnot_YI: 0.0006670
+    p_cnot_YX: 0.0006670
+    p_cnot_YY: 0.0006670
+    p_cnot_YZ: 0.0006670
+    p_cnot_ZI: 0.0006670
+    p_cnot_ZX: 0.0006670
+    p_cnot_ZY: 0.0006670
+    p_cnot_ZZ: 0.0006670

configs/config_qadapt_t2_cnot_1p5.yaml

@@ -0,0 +1,40 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# Shared QAdapt T2 CNOT-noise task.
+
+model_id: 111
+distance: 9
+n_rounds: 9
+
+workflow:
+  task: inference
+
+data:
+  code_rotation: O1
+  noise_model:
+    p_prep_X: 0.0010000
+    p_prep_Z: 0.0010000
+    p_meas_X: 0.0100000
+    p_meas_Z: 0.0100000
+    p_idle_cnot_X: 0.0003330
+    p_idle_cnot_Y: 0.0003330
+    p_idle_cnot_Z: 0.0003330
+    p_idle_spam_X: 0.0006670
+    p_idle_spam_Y: 0.0006670
+    p_idle_spam_Z: 0.0006670
+    p_cnot_IX: 0.0010005
+    p_cnot_IY: 0.0010005
+    p_cnot_IZ: 0.0010005
+    p_cnot_XI: 0.0010005
+    p_cnot_XX: 0.0010005
+    p_cnot_XY: 0.0010005
+    p_cnot_XZ: 0.0010005
+    p_cnot_YI: 0.0010005
+    p_cnot_YX: 0.0010005
+    p_cnot_YY: 0.0010005
+    p_cnot_YZ: 0.0010005
+    p_cnot_ZI: 0.0010005
+    p_cnot_ZX: 0.0010005
+    p_cnot_ZY: 0.0010005
+    p_cnot_ZZ: 0.0010005

configs/config_qadapt_t3_idle_1p5.yaml

@@ -0,0 +1,40 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# Shared QAdapt T3 idle-noise task.
+
+model_id: 111
+distance: 9
+n_rounds: 9
+
+workflow:
+  task: inference
+
+data:
+  code_rotation: O1
+  noise_model:
+    p_prep_X: 0.0010000
+    p_prep_Z: 0.0010000
+    p_meas_X: 0.0100000
+    p_meas_Z: 0.0100000
+    p_idle_cnot_X: 0.0004995
+    p_idle_cnot_Y: 0.0004995
+    p_idle_cnot_Z: 0.0004995
+    p_idle_spam_X: 0.0010005
+    p_idle_spam_Y: 0.0010005
+    p_idle_spam_Z: 0.0010005
+    p_cnot_IX: 0.0006670
+    p_cnot_IY: 0.0006670
+    p_cnot_IZ: 0.0006670
+    p_cnot_XI: 0.0006670
+    p_cnot_XX: 0.0006670
+    p_cnot_XY: 0.0006670
+    p_cnot_XZ: 0.0006670
+    p_cnot_YI: 0.0006670
+    p_cnot_YX: 0.0006670
+    p_cnot_YY: 0.0006670
+    p_cnot_YZ: 0.0006670
+    p_cnot_ZI: 0.0006670
+    p_cnot_ZX: 0.0006670
+    p_cnot_ZY: 0.0006670
+    p_cnot_ZZ: 0.0006670

configs/config_qadapt_t4_z_bias_1p5.yaml

@@ -0,0 +1,40 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# Shared QAdapt T4 Z-biased-noise task.
+
+model_id: 111
+distance: 9
+n_rounds: 9
+
+workflow:
+  task: inference
+
+data:
+  code_rotation: O1
+  noise_model:
+    p_prep_X: 0.0015000
+    p_prep_Z: 0.0010000
+    p_meas_X: 0.0150000
+    p_meas_Z: 0.0100000
+    p_idle_cnot_X: 0.0003330
+    p_idle_cnot_Y: 0.0003330
+    p_idle_cnot_Z: 0.0004995
+    p_idle_spam_X: 0.0006670
+    p_idle_spam_Y: 0.0006670
+    p_idle_spam_Z: 0.0010005
+    p_cnot_IX: 0.0006670
+    p_cnot_IY: 0.0006670
+    p_cnot_IZ: 0.0010005
+    p_cnot_XI: 0.0006670
+    p_cnot_XX: 0.0006670
+    p_cnot_XY: 0.0006670
+    p_cnot_XZ: 0.0010005
+    p_cnot_YI: 0.0006670
+    p_cnot_YX: 0.0006670
+    p_cnot_YY: 0.0006670
+    p_cnot_YZ: 0.0010005
+    p_cnot_ZI: 0.0010005
+    p_cnot_ZX: 0.0010005
+    p_cnot_ZY: 0.0010005
+    p_cnot_ZZ: 0.0010005

evaluation.json

@@ -0,0 +1,97 @@
+{
+  "schema_version": 1,
+  "release_version": "v1",
+  "models": {
+    "ising-fast-t0-e100": {
+      "model_id": 1,
+      "parameters": 912772,
+      "source_checkpoint_sha256": "c23c9b1507cf4d213ce1b6526f87ee9e2f9db2bd8dc336c16f915b77724a0661"
+    },
+    "qadapt-e100": {
+      "model_id": 111,
+      "parameters": 650374,
+      "source_checkpoint_sha256": "59d55a948a1a99458f7ccb0bba83335a779ef90eb8b36125407b65d45d771f9f"
+    }
+  },
+  "terminal_t0_t4_d9_r9": {
+    "scope": "Release-weight coverage; not the paper's T0 architecture table.",
+    "shots_per_basis_per_task": 262144,
+    "seed": 12345,
+    "logical_error_rate": {
+      "pymatching": {
+        "T0": 0.04503440856933594,
+        "T1": 0.05488777160644531,
+        "T2": 0.1540374755859375,
+        "T3": 0.0499725341796875,
+        "T4": 0.098114013671875,
+        "mean": 0.08040924072265625
+      },
+      "ising-fast-t0-e100": {
+        "T0": 0.040943145751953125,
+        "T1": 0.052066802978515625,
+        "T2": 0.14017486572265625,
+        "T3": 0.04532432556152344,
+        "T4": 0.0913543701171875,
+        "mean": 0.07397270202636719
+      },
+      "qadapt-e100": {
+        "T0": 0.036121368408203125,
+        "T1": 0.046192169189453125,
+        "T2": 0.13011550903320312,
+        "T3": 0.04052734375,
+        "T4": 0.08282089233398438,
+        "mean": 0.06715545654296876
+      }
+    },
+    "qadapt_reduction_vs_ising_fast_mean_percent": 9.215893561612038
+  },
+  "paper_selected_ood": {
+    "protocol": "11 axis combinations x 5 multipliers x d={7,9}; X/Z pooled",
+    "shots_per_basis_per_configuration": 262144,
+    "d7": {
+      "configurations": 55,
+      "ising_fast_ler_mean": 0.23447262157093396,
+      "qadapt_ler_mean": 0.22700611461292614,
+      "qadapt_reduction_percent": 3.184383280224047,
+      "qadapt_wins": 55,
+      "ising_fast_backend_latency_us_per_round_mean": 2.3286138533266505,
+      "qadapt_backend_latency_us_per_round_mean": 2.195449274578931
+    },
+    "d9": {
+      "configurations": 55,
+      "ising_fast_ler_mean": 0.24443563981489702,
+      "qadapt_ler_mean": 0.2365336678244851,
+      "qadapt_reduction_percent": 3.232741345082013,
+      "qadapt_wins": 55,
+      "ising_fast_backend_latency_us_per_round_mean": 4.883934545490627,
+      "qadapt_backend_latency_us_per_round_mean": 4.6081848113299015
+    }
+  },
+  "paper_willow_zero_shot": {
+    "rounds": 10,
+    "fine_tuning": false,
+    "d5": {
+      "shots": 400000,
+      "ising_fast_ler": 0.09963,
+      "qadapt_ler": 0.0938575,
+      "qadapt_reduction_percent": 5.79393756900532,
+      "ising_fast_backend_latency_us_per_round": 0.704,
+      "qadapt_backend_latency_us_per_round": 0.694
+    },
+    "d7": {
+      "shots": 100000,
+      "ising_fast_ler": 0.08412,
+      "qadapt_ler": 0.08201,
+      "qadapt_reduction_percent": 2.5083214455539715,
+      "ising_fast_backend_latency_us_per_round": 1.405,
+      "qadapt_backend_latency_us_per_round": 1.274
+    }
+  },
+  "paper_t0_architecture_table": {
+    "included_for_context_only": true,
+    "warning": "The paper table used Ising-fast e53 and T0-only HTNet e89, not either final e100 release checkpoint.",
+    "d7": {"ising_fast_ler": 0.05071, "htnet_ler": 0.04280},
+    "d9": {"ising_fast_ler": 0.04037, "htnet_ler": 0.03286}
+  },
+  "latency_scope": "PyMatching residual decode only; excludes neural inference, data movement, and residual construction."
+}

examples/infer_ood.py

@@ -0,0 +1,123 @@
+#!/usr/bin/env python3
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Run released pre-decoders on the fixed training-axis OOD grid."""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+
+CODE_ROOT = Path(__file__).resolve().parents[1]
+if str(CODE_ROOT) not in sys.path:
+    sys.path.insert(0, str(CODE_ROOT))
+
+from scripts.experiments.unknown_noise.generate_unknown_axismix_grid_u1p2_5p0_configs import (  # noqa: E402
+    write_axismix_grid_configs,
+)
+from scripts.qadapt_example_utils import (  # noqa: E402
+    InferenceJob,
+    add_common_inference_args,
+    build_paired_command,
+    parse_gpus,
+    run_jobs,
+)
+
+
+PAPER_DISTANCES = (7, 9)
+PAPER_MULTIPLIERS = (1.2, 1.5, 2.0, 2.5, 3.0)
+
+
+def parse_distances(value: str) -> list[int]:
+    result = [int(item.strip()) for item in value.split(",") if item.strip()]
+    if not result or result != sorted(set(result)):
+        raise argparse.ArgumentTypeError(
+            "distances must be a non-empty, increasing comma-separated list"
+        )
+    return result
+
+
+def parse_multipliers(value: str) -> list[float]:
+    result = [float(item.strip()) for item in value.split(",") if item.strip()]
+    if not result or result != sorted(set(result)) or any(item <= 0 for item in result):
+        raise argparse.ArgumentTypeError(
+            "multipliers must be a non-empty, increasing comma-separated list "
+            "of positive numbers"
+        )
+    return result
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--distances",
+        type=parse_distances,
+        default=list(PAPER_DISTANCES),
+        help="Comma-separated distances; defaults to the paper's d=7,9 grid.",
+    )
+    parser.add_argument("--n-rounds", type=int, default=9)
+    parser.add_argument(
+        "--multipliers",
+        type=parse_multipliers,
+        default=list(PAPER_MULTIPLIERS),
+        help="Comma-separated OOD multipliers; defaults to the paper's 1.2--3.0 grid.",
+    )
+    parser.add_argument(
+        "--generated-config-dir",
+        type=Path,
+        default=Path("outputs/generated_configs/ood"),
+    )
+    parser.add_argument(
+        "--manifest",
+        type=Path,
+        default=Path("outputs/generated_configs/ood/manifest.json"),
+    )
+    add_common_inference_args(
+        parser,
+        default_output_dir=Path("outputs/examples/released_models/ood"),
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    _, manifest = write_axismix_grid_configs(
+        base_config="conf/examples/qadapt/config_qadapt_t0_base.yaml",
+        output_dir=args.generated_config_dir,
+        manifest=args.manifest,
+        grid_multipliers=args.multipliers,
+    )
+    jobs = []
+    for distance in args.distances:
+        for environment in manifest["environments"]:
+            config_file = args.generated_config_dir / environment["config_filename"]
+            label = (
+                f"d{distance}_{environment['env_key']}_"
+                f"{environment['multiplier_key']}"
+            )
+            output_path = args.output_dir / f"d{distance}" / f"{label}.json"
+            jobs.append(
+                InferenceJob(
+                    label=label,
+                    command=build_paired_command(
+                        args,
+                        config_file=config_file,
+                        output_path=output_path,
+                        distance=distance,
+                        n_rounds=args.n_rounds,
+                    ),
+                    output_path=output_path,
+                )
+            )
+    run_jobs(
+        jobs,
+        gpus=parse_gpus(args.gpus),
+        parallelism=args.parallelism,
+        resume=args.resume,
+        dry_run=args.dry_run,
+    )
+
+
+if __name__ == "__main__":
+    main()

examples/infer_t0_t4.py

@@ -0,0 +1,109 @@
+#!/usr/bin/env python3
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Run released pre-decoders on the five T0-T4 simulated noise tasks."""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+
+CODE_ROOT = Path(__file__).resolve().parents[1]
+if str(CODE_ROOT) not in sys.path:
+    sys.path.insert(0, str(CODE_ROOT))
+
+from scripts.qadapt_example_utils import (  # noqa: E402
+    InferenceJob,
+    TASK_CONFIGS,
+    add_common_inference_args,
+    build_paired_command,
+    parse_gpus,
+    run_jobs,
+)
+
+
+TASK_BY_ID = {
+    f"T{index}": (task_key, config_name)
+    for index, (task_key, config_name) in enumerate(TASK_CONFIGS)
+}
+
+
+def parse_distances(value: str) -> list[int]:
+    result = [int(item.strip()) for item in value.split(",") if item.strip()]
+    if not result or result != sorted(set(result)):
+        raise argparse.ArgumentTypeError(
+            "distances must be a non-empty, increasing comma-separated list"
+        )
+    return result
+
+
+def parse_tasks(value: str) -> list[str]:
+    result = [item.strip().upper() for item in value.split(",") if item.strip()]
+    if not result or len(result) != len(set(result)):
+        raise argparse.ArgumentTypeError(
+            "tasks must be a non-empty comma-separated subset of T0,T1,T2,T3,T4"
+        )
+    unknown = [item for item in result if item not in TASK_BY_ID]
+    if unknown:
+        raise argparse.ArgumentTypeError(f"unknown task(s): {','.join(unknown)}")
+    return result
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--distances",
+        type=parse_distances,
+        default=[9],
+        help=(
+            "Comma-separated distances. Use 7,9 with --tasks T0 for the "
+            "paper's mapped-noise geometry; the default is release coverage at d=9."
+        ),
+    )
+    parser.add_argument(
+        "--tasks",
+        type=parse_tasks,
+        default=list(TASK_BY_ID),
+        help="Comma-separated task subset; defaults to T0,T1,T2,T3,T4.",
+    )
+    parser.add_argument("--n-rounds", type=int, default=9)
+    add_common_inference_args(
+        parser,
+        default_output_dir=Path("outputs/examples/released_models/t0_t4"),
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    jobs = []
+    for distance in args.distances:
+        for task_id in args.tasks:
+            task_key, config_name = TASK_BY_ID[task_id]
+            label = f"d{distance}_{task_key}"
+            output_path = args.output_dir / f"d{distance}" / f"{task_key}.json"
+            jobs.append(
+                InferenceJob(
+                    label=label,
+                    command=build_paired_command(
+                        args,
+                        config_name=config_name,
+                        output_path=output_path,
+                        distance=distance,
+                        n_rounds=args.n_rounds,
+                    ),
+                    output_path=output_path,
+                )
+            )
+    run_jobs(
+        jobs,
+        gpus=parse_gpus(args.gpus),
+        parallelism=args.parallelism,
+        resume=args.resume,
+        dry_run=args.dry_run,
+    )
+
+
+if __name__ == "__main__":
+    main()

examples/infer_willow.py

@@ -0,0 +1,115 @@
+#!/usr/bin/env python3
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Reproduce the paper's d=5/d=7, ten-round Google Willow evaluation."""
+
+from __future__ import annotations
+
+import argparse
+import os
+import shlex
+import sys
+from pathlib import Path
+
+CODE_ROOT = Path(__file__).resolve().parents[1]
+if str(CODE_ROOT) not in sys.path:
+    sys.path.insert(0, str(CODE_ROOT))
+
+from scripts.qadapt_example_utils import (  # noqa: E402
+    add_common_inference_args,
+    checkpoint_specs,
+    parse_gpus,
+)
+
+
+def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--benchmark-root",
+        type=Path,
+        default=Path("benchmarks/google_qec/google_105Q_surface_code_d3_d5_d7"),
+    )
+    parser.add_argument(
+        "--distances",
+        nargs="+",
+        type=int,
+        default=[5, 7],
+        help="Paper default: d=5 and d=7.",
+    )
+    parser.add_argument(
+        "--rounds",
+        nargs="+",
+        type=int,
+        default=[10],
+        help="Paper default: ten syndrome-extraction rounds.",
+    )
+    add_common_inference_args(
+        parser,
+        default_output_dir=Path("outputs/examples/released_models/willow"),
+        default_num_samples=0,
+    )
+    return parser.parse_args(argv)
+
+
+def main(argv: list[str] | None = None) -> int:
+    args = parse_args(argv)
+    output_path = args.output_dir / "results.json"
+    if args.resume and output_path.is_file():
+        print(f"[resume] output exists: {output_path}")
+        return 0
+
+    selected_gpus = parse_gpus(args.gpus)
+    bases = ["X", "Z"] if args.basis == "both" else [args.basis]
+    specs = checkpoint_specs(args)
+    command_preview = [
+        str(args.python),
+        "-m",
+        "scripts.providers.google_qec_decoder_benchmark",
+        "--benchmark-root",
+        str(args.benchmark_root),
+        "--distances",
+        *(str(value) for value in args.distances),
+        "--rounds",
+        *(str(value) for value in args.rounds),
+        "--bases",
+        *bases,
+        "--models",
+        *(spec.name for spec in specs),
+        "--max-shots",
+        str(args.num_samples),
+        "--batch-size",
+        str(args.batch_size),
+        "--latency-shots",
+        str(args.latency_num_samples),
+        "--output",
+        str(output_path),
+    ]
+    if args.dry_run:
+        print(
+            f"[dry-run] gpu={selected_gpus[0]} seed={args.seed} "
+            + shlex.join(command_preview)
+        )
+        for spec in specs:
+            print(
+                f"[dry-run] model {spec.name}: "
+                f"model_id={spec.model_id} checkpoint={spec.checkpoint}"
+            )
+        return 0
+
+    os.environ["CUDA_VISIBLE_DEVICES"] = selected_gpus[0]
+    from scripts.providers import google_qec_decoder_benchmark as benchmark
+
+    benchmark.DEFAULT_MODELS = {
+        spec.name: benchmark.BenchmarkModel(
+            spec.name,
+            spec.model_id,
+            spec.checkpoint,
+        )
+        for spec in specs
+    }
+    benchmark.DEFAULT_BENCHMARK_ROOT = args.benchmark_root
+    return benchmark.main(command_preview[3:])
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65f979c9f23f1b13b76876c6e5df204d1bd28b0e3322483d966db0efb840600d
+size 2610600