README.md

---
license: mit
library_name: coreml
pipeline_tag: text-classification
base_model: cua-ai/cua-s1-forms
base_model_relation: quantized
datasets:
- cua-ai/cua-s1-forms
tags:
- coreml
- apple-silicon
- computer-use
- classification
- cua-s1
- fp16
---

# CUA-S1-FORMS — Core ML

FP16 Core ML conversion of [Cua's CUA-S1-FORMS](https://huggingface.co/cua-ai/cua-s1-forms),
a **706,048-parameter** specialist that selects among supplied form actions.
The portable package is **1,511,163 bytes (1.51 MB)**. No text generation, KV cache,
or external tokenizer is required.

The model uses small Transformer encoders over UTF-8 bytes and an attention
readout. It is a classifier, not an autoregressive LLM. The inherited checkpoint
configuration contains an unused `hf_model` value; this `tinyx` checkpoint does
not load Qwen weights.

## Files

| File | Purpose |
| --- | --- |
| `cua_s1_forms_fp16_options32.mlpackage/` | Portable model; compile locally or add to Xcode |
| `cua_s1_forms_fp16_options32.mlmodelc/` | Compiled bundle for FluidAudio's model loader |
| `preprocessing.py` | Upstream-compatible byte encoding and input validation |
| `conversion.json` | Architecture, conversion versions, and portable package hashes |
| `assets.lock.json` | Pinned upstream source, model, and demo hashes |
| `checksums.json` | SHA-256 of each distributed file except this checksum file |
| `reports/` | Per-row parity, original Cua metrics, and compute-placement report |

The model targets **iOS 17/macOS 14 or newer**. Runtime validation used an Apple
silicon Mac. Use the portable package for local compilation on other supported
systems; iPhone performance and compatibility of the precompiled bundle across
older OS versions have not been measured.

## Python usage

Install `coremltools==9.0`, `numpy==1.26.4`, and `huggingface_hub` on macOS.
Download this repository, then run from its directory:

```python
import coremltools as ct
from preprocessing import InputLimits, prepare_inputs

model = ct.models.MLModel(
    "cua_s1_forms_fp16_options32.mlpackage",
    compute_units=ct.ComputeUnit.CPU_AND_NE,
)
options = ["fill E-mail: person@example.com", "check", "click", "skip"]
inputs = prepare_inputs(
    'TASK fill the form from the document, then submit\n'
    'FORM Contact details\nELEMENT Edit "Email address" value=""',
    options,
    InputLimits(),
)
probabilities = model.predict(inputs)["probabilities"][0, :len(options)]
print(options[int(probabilities.argmax())], probabilities)
```

During review, download the model PR's revision (for example `--revision refs/pr/1`)
with `hf download FluidInference/cua-s1-forms-coreml --local-dir ./cua-coreml`.
After merge, the default `main` revision contains the artifacts.

## Swift usage

The proposed [FluidAudio integration](https://github.com/FluidInference/FluidAudio/tree/codex/cua-s1-forms)
provides `CuaS1FormsManager`:

```swift
import FluidAudio
import Foundation

let manager = try await CuaS1FormsManager.load(
    from: URL(fileURLWithPath: "/models/cua_s1_forms_fp16_options32.mlpackage"))
let decision = try await manager.score(
    context: "TASK fill the form from the document, then submit\nFORM Contact details\nELEMENT Edit \"Email address\" value=\"\"",
    options: ["fill E-mail: person@example.com", "check", "click", "skip"])
print(decision.selectedOption, decision.probabilities)
```

After the model and Swift PRs land, `try await CuaS1FormsManager.load()` downloads
and caches the compiled artifact automatically.

## Tensor interface

| Name | Type | Shape |
| --- | --- | --- |
| `context_ids` | int32 | `[1, 224]` |
| `option_ids` | int32 | `[1, 32, 96]` |
| `option_mask` | int32 | `[1, 32]` |
| `logits` | float32 output | `[1, 32]` |
| `probabilities` | float32 output | `[1, 32]` |

Encode UTF-8 bytes plus one, pad with zero, and truncate by bytes at 224 for
context and 96 per option. Supply a nonempty context and 2–32 nonempty options.
Set option-mask entries to one for supplied options and zero for padding.
Padded logits are `-10000`; padded probabilities are zero. Inputs above 32
options must be rejected or use a separately exported larger-capacity model.
The example helper rejects overflow rather than dropping choices.

## Conversion verification

On the complete pinned **196-row upstream demo**, PyTorch and Core ML both
selected **196/196 labeled options correctly**, and matched each other's selected
option on every row. The unmodified upstream Cua evaluator reports 36 fill,
4 check, 6 click, and 150 skip decisions, with zero wrong actions, wrong targets,
or unsafe actions on these saved predictions. It counts `skip` as abstention,
so its 23.47% coverage corresponds to 46 actionable decisions.

| Check | Core ML `ALL` | Core ML `CPU_AND_NE` |
| --- | ---: | ---: |
| Selected options matching PyTorch | 196 / 196 | 196 / 196 |
| Maximum absolute probability error | 0.003099 | 0.002336 |
| Warm model-call median | 1.85 ms | 0.90 ms |
| Warm model-call p95 | 2.49 ms | 0.94 ms |

Measured September 19, 2026 on Apple M5 Pro, 24 GB, macOS 27.0, using
Python 3.11.11, PyTorch 2.7.0, and coremltools 9.0. Timing is exploratory and
includes Python call overhead; model loading, encoding, document extraction,
UI observation, and action execution are excluded. It is not an optimized
PyTorch/MPS speed comparison.

The conversion gates require 100% selected-option agreement, no accuracy loss,
maximum absolute probability error ≤ 0.005, finite outputs, normalized live
probabilities, and zero probability for padding. The FP32 export adapter differs
from the unmodified PyTorch reference by at most 0.00000113. Six reversed-option
checks also pass on each Core ML configuration. The checked-in placement report
counts 149 Neural Engine operations, 24 CPU operations, and zero GPU operations;
operation counts are not a measurement of time spent on each processor.

These results verify conversion on three demo forms and three PDFs. They do not
establish generalization, live GUI completion rates, or production safety.
The original demo is not redistributed here; its exact revision and SHA-256 are
in the asset lock. No training or larger-corpus evaluation was performed for
this conversion.

## ANE profile

A separate September 19 profile uses the same portable-package hashes on the M5
Pro, with real demo rows 0, 68, and 130 (27, 21, and 19 options). Each policy
runs two warmup passes and ten timed passes, totaling 30 timed predictions. All
120 timed predictions select the correct labels.

| Policy | CPU ops | GPU ops | ANE ops | Warm p50 | Warm p95 |
| --- | ---: | ---: | ---: | ---: | ---: |
| `CPU_ONLY` | 173 | 0 | 0 | 1.527 ms | 1.602 ms |
| `CPU_AND_GPU` | 0 | 173 | 0 | 0.929 ms | 2.380 ms |
| `CPU_AND_NE` | 24 | 0 | 149 | 0.929 ms | 0.973 ms |
| `ALL` | 0 | 173 | 0 | 0.912 ms | 1.229 ms |

`CPU_AND_NE` assigns 86.1% of operations to ANE; `ALL` chooses the GPU on this
Mac. CPU fallbacks cover integer/mask preparation and embedding gathers. These
are public `MLComputePlan` preferred-device assignments, not measurements of
utilization, energy, or time spent on each device. No Instruments runtime trace
was captured.

ANE model loading took 566.8 ms, followed by a 1.65 ms first prediction, with
system caches retained. These are not first-install cold-start numbers. Warm
timing includes Python model-call overhead and excludes encoding, Swift/UI work,
and animation. This three-row timing manifest differs from the full conversion
parity run above; no weights or graph were changed.

See [reports/ane-profile.json](reports/ane-profile.json) for all operation
assignments, individual timings, hashes, and the protocol;
[reports/ane-fallback.json](reports/ane-fallback.json) records rejection reasons.
Reproduce with `uv run --frozen python profile-coreml.py` in the
[Mobius conversion directory](https://github.com/FluidInference/mobius/tree/codex/cua-s1-forms/models/computer-use/cua-s1-forms/coreml).

## Optional higher-ANE variant

The `ane-gather/` directory contains an alternative portable package and compiled
bundle with the **same int32 inputs and float32 outputs** and all trained weights.
The variant uses shared float16 mask inputs and unsigned 16-bit embedding indices
to eliminate negative-index correction and place the gathers on ANE. Valid byte
IDs 0–256 remain exact. It is **1,509,491 bytes** as a portable package.

On this M5 Pro, the scheduler plan is **162 ANE operations and 3 CPU input casts
(98.2% ANE)** for both `CPU_AND_NE` and `ALL`. The default model has 149 ANE and
24 CPU operations (86.1%) under `CPU_AND_NE`. Counts are not runtime or energy
shares, and host byte encoding still runs outside the model.

The optional variant passes **196/196 decisions** against upstream on `ALL` and
`CPU_AND_NE`, with maximum probability error **0.002336** under the unchanged
0.005 tolerance. **28 Python regression tests** pass, including all byte-ID
boundaries, full option capacity, truncation, and reordered choices. The Swift
manager independently passes all 196 reference decisions, compiled-cache loading,
and concurrent/reordered requests; the native demo passes its three-form checks.

A matched same-process ABBA comparison uses three real inputs and 60 timed calls
per model after warmup:

| Artifact | CPU ops | ANE ops | Warm p50 | Warm p95 |
| --- | ---: | ---: | ---: | ---: |
| Root/default | 24 | 149 | 0.915 ms | 0.968 ms |
| `ane-gather/` | 3 | 162 | 0.970 ms | 0.988 ms |

Higher ANE placement is about **6% slower** in this local comparison, so the
root/default artifact remains unchanged. No energy or CPU-time saving is claimed.
The original input names, dtypes, shapes, and byte encoding still apply. Load
`ane-gather/cua_s1_forms_fp16_options32.mlpackage` with the existing Python or
Swift APIs, or pass its local path to the Swift demo's `--model` argument.

Reports: [parity](reports/ane-gather-verification.json),
[Swift validation](reports/swift-ane-validation.json),
[compute plans](reports/ane-gather-profile.json),
[fallbacks](reports/ane-gather-fallback.json), and
[matched comparison](reports/ane-comparison.json). Reproduce with
`uv run --frozen python convert-coreml.py --optimization ane-gather --output-dir build/ane-gather`
in the [Mobius conversion directory](https://github.com/FluidInference/mobius/tree/codex/cua-s1-forms/models/computer-use/cua-s1-forms/coreml#optional-higher-ane-variant).

## Application responsibilities

The application must extract document entities, describe UI elements, build
candidate actions, and validate and order the selected actions. Submission and
other effects require application authorization. Scores are not calibrated
confidence guarantees. Text outside the byte limits is truncated, and arbitrary
new forms and languages require their own evaluation.

## Source, reproduction, and license

The [Mobius conversion toolkit](https://github.com/FluidInference/mobius/tree/codex/cua-s1-forms/models/computer-use/cua-s1-forms/coreml)
contains the conversion code, lockfile, original reference implementation and
evaluator, tests, and full reproduction instructions. Adaptations are limited
to export-compatible masking, a floating-point clamp constant, finite padded
logits, and disabling the fused PyTorch Transformer fast path during tracing.
All trained layers and checkpoint tensors are retained; internal compute and
weights are converted to FP16.

- Model: [`cua-ai/cua-s1-forms` at `f54adbf447f4ca6ec259f529ee3f2e3e09f8cc71`](https://huggingface.co/cua-ai/cua-s1-forms/tree/f54adbf447f4ca6ec259f529ee3f2e3e09f8cc71).
- Demo: [`cua-ai/cua-s1-forms` at `8273f34778b99ac2e12d9f6e7d57dad99ae20845`](https://huggingface.co/datasets/cua-ai/cua-s1-forms/tree/8273f34778b99ac2e12d9f6e7d57dad99ae20845).
- Code: [`trycua/cua` at `83f142c4290a0f7d9ed545ae8532858c6e4f8145`](https://github.com/trycua/cua/tree/83f142c4290a0f7d9ed545ae8532858c6e4f8145/libs/cua-s1).

The pinned model and dataset cards declare MIT. See [LICENSE](LICENSE),
[NOTICES.md](NOTICES.md), and the preserved
[upstream third-party notices](UPSTREAM-THIRD-PARTY-NOTICES.md).