---
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 |
| `synthetic-test.lock.json` | Full published synthetic test revision, count, size, and SHA-256 |
| `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:

```bash
hf download FluidInference/cua-s1-forms-coreml --revision refs/pr/1 --local-dir ./cua-coreml
cd 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 was performed. The separate full synthetic test below evaluates
the unchanged artifacts and exposes numerical failures outside this demo.

## Full published synthetic test

Evaluated the complete published synthetic [`test.jsonl`](https://huggingface.co/datasets/cua-ai/cua-s1-forms/blob/8273f34778b99ac2e12d9f6e7d57dad99ae20845/test.jsonl):
**24,370 decisions across 1,040 episode seeds**, with no excluded rows or truncated
inputs. The dataset revision is `8273f34778b99ac2e12d9f6e7d57dad99ae20845`; its
SHA-256 is `d63a7e0db195d4d20154a40b2f8dd09ce3bb65487a158c638da5c609d4475e7c`.
The upstream [model card](https://huggingface.co/cua-ai/cua-s1-forms/blob/f54adbf447f4ca6ec259f529ee3f2e3e09f8cc71/README.md) reports 99.95% on an approximately 15,000-row
synthetic test. Our result rounds to that accuracy, but the released file contains
24,370 rows; this does not reconstruct the card's unspecified smaller manifest.

| Model / backend | Correct decisions | Top-1 accuracy | Median call | p95 call |
| --- | ---: | ---: | ---: | ---: |
| Upstream PyTorch / CPU | 24,359 / 24,370 | 99.9549% | 1.787 ms | 3.104 ms |
| Original FP16 Core ML / CPU + ANE | 24,359 / 24,370 | 99.9549% | 1.003 ms | 1.133 ms |
| ANE-gather FP16 Core ML / CPU + ANE | 24,359 / 24,370 | 99.9549% | 1.052 ms | 1.177 ms |

Both Core ML exports select the same option as PyTorch on **all 24,370 rows**.
All three share the same 11 errors: choosing `fill` when the label is `skip`.
The original Cua evaluator counts these as wrong/unsafe actions; this offline
benchmark executes no actions. All 9,802 fill, 816 check, and 1,040 click labels
are correct; skip accuracy is 12,701/12,712. Higher ANE placement is approximately
**4.8% slower** by median here, so the original remains the default.

**Strict numerical conversion parity fails for both exports.** Each has 11 rows
above the original 0.005 absolute probability-error limit, with a maximum error
of **0.0204874**. One further row (zero-based index 19270) has a live-probability
sum of **0.99893665**. That lies outside the current Swift manager's 0.001
normalization guard, so this output would be rejected despite its correct argmax.
These are raw model-output accuracy results, not a claim that every row succeeds
through the Swift API. No probabilities were renormalized and no tolerances or
model weights were changed. The earlier 196-row demo passed its numerical gates;
the larger split exposes failures that demo did not cover.

Measured September 19, 2026 on **Apple M5 Pro, 24 GB, macOS 27.0 (26A428)**,
Python 3.11.11, PyTorch 2.7.0, coremltools 9.0. Batch size 1, three warmup rows per
model, one timed pass over the whole split; both Core ML models remain loaded
and alternate AB/BA order by row. PyTorch uses two CPU threads and one inter-op
thread, with the Transformer fast path disabled. Timers cover PyTorch
forward + softmax or synchronous Core ML prediction, excluding encoding,
validation, loading, UI, and network. These compare deployment backends, not
algorithms on equal hardware, and differ from the separate Swift timings at the end of this card.

Upstream describes this synthetic split as disjoint from training/validation by
form signature; those signatures were not independently re-audited here. No
training, validation inference, test-based tuning, or hosted Jev/API comparison
was performed. This measures supplied-option classification, not unseen real-world
GUI completion or document extraction.

[Full report](reports/synthetic-test.json), [complete compressed per-row trace](reports/synthetic-test-decisions.jsonl.gz),
and [test manifest](synthetic-test.lock.json) retain the exact protocol, hashes,
paired decisions, action metrics, and all failed numerical checks. Reproduce with
`benchmark-synthetic.py --require-parity` in the [Mobius toolkit](https://github.com/FluidInference/mobius/tree/codex/cua-s1-forms/models/computer-use/cua-s1-forms/coreml#full-published-synthetic-test).
That command exits 1 for the recorded numerical failures; it does not normalize
scores or weaken the original gates. Treat these artifacts as under review
until the numerical failures are resolved and validated.

## 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).

## INT8 weight trial

A matched run over all **24,370 synthetic decisions** on the same M5 Pro:

| Export | Package size | Accuracy | Median | p95 |
| --- | ---: | ---: | ---: | ---: |
| Original FP16 | 1.51 MB | 99.9549% | 0.990 ms | 1.102 ms |
| INT8 weights, FP16 compute | 0.81 MB | 99.9549% | 0.990 ms | 1.104 ms |

**46.2% smaller**, with every selected option unchanged and effectively identical
latency. Numerical parity still fails: 64 rows exceed the 0.005 probability-error
limit (maximum 0.067738), versus 11 for FP16. No INT8 probability-sum violations
were observed. The original remains the default.

The experimental `int8-weights/cua_s1_forms_int8_options32.mlpackage` uses INT8
weights with FP16 computation. Compile it locally or load it with the existing
Swift `load(from:)` API. Inputs and outputs match the original model. This
variant was measured on M5 Pro; iPhone behavior has not been measured.

[Full report](reports/int8-synthetic-test.json) ·
[Complete per-row trace](reports/int8-synthetic-test-decisions.jsonl.gz) ·
[Quantization manifest](int8-weights/conversion.json) ·
[Reproduction](https://github.com/FluidInference/mobius/tree/codex/cua-s1-forms/models/computer-use/cua-s1-forms/coreml#int8-weight-trial)

The matched benchmark uses CPU+ANE, batch 1, three warmups/model, and one complete
pass with alternating Core ML order; encoding/loading/UI are excluded. All models
get the same 24,359 decisions correct. Public compute plans assign 149 ANE and
32 CPU operations, with 19 unassigned constant-dequantization operations. This
compression does not establish an INT8 activation path or a speed/energy gain.
No calibration, training, or test-based tuning was performed. Numerical parity
remains failed; the default FP16 artifacts are unchanged.

## INT4 weight trial

A matched run over all **24,370 synthetic decisions** on M5 Pro, CPU+ANE:

| Export | Package size | Accuracy | Median | p95 |
| --- | ---: | ---: | ---: | ---: |
| FP16 control, iOS 18 target | 1.51 MB | 99.9549% | 0.982 ms | 1.081 ms |
| INT4 weights, FP16 compute | 0.45 MB | 99.9302% | 0.982 ms | 1.082 ms |

**70.1% smaller**, with essentially unchanged latency. INT4 makes **17 errors
versus 11** for FP16: 14 choices change, introducing 10 errors and correcting four.
Numerical parity fails: 356 rows exceed the 0.005 probability-error limit
(maximum 0.754359); no INT4 probability-sum violations were observed.

Packed INT4 requires **iOS 18/macOS 15**. Both exports use the same decomposed
attention graph and FP16 computation. Batch-1 timing excludes encoding/loading/UI.
INT8 preserves all choices at 0.81 MB; the original FP16 remains the default.

The experimental INT4 package is
`int4-weights/cua_s1_forms_int4_options32.mlpackage`. Its matched FP16 control is
`int4-source-fp16/cua_s1_forms_fp16_options32.mlpackage`. Nineteen weight tensors
use per-channel symmetric INT4 with a 2,048-element threshold; no calibration or
retraining. This is weight compression with FP16 computation. The graph is
retargeted from the verified original to preserve decomposed attention.

[Full report](reports/int4-synthetic-test.json) ·
[Complete per-row trace](reports/int4-synthetic-test-decisions.jsonl.gz) ·
[Quantization manifest](int4-weights/conversion.json) ·
[FP16 control manifest](int4-source-fp16/conversion.json) ·
[Reproduction](https://github.com/FluidInference/mobius/tree/codex/cua-s1-forms/models/computer-use/cua-s1-forms/coreml#int4-weight-trial)

The demo retains 196/196 choices but fails numerical parity. The full benchmark
above exposes the accuracy loss. Public compute-plan placement is 149 ANE / 32 CPU
operations, plus 19 unassigned constant dequantizations; no speed/energy claim.

## 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).

## Live browser proof and expanded Swift benchmark

[![Actual CUA-driven browser forms](demo/browser-demo.gif)](demo/browser-demo.mp4)

The [native Swift browser demo](https://github.com/FluidInference/FluidAudio/tree/7f9eb92b0af8594c4e048a9e57f697340aacfa67/Examples/CuaS1FormsDemo)
loads both variants into independent WKWebViews. It reads actual DOM labels,
roles and state, asks the model for a choice, applies compatible fill/check
actions, dispatches events, and independently verifies the resulting DOM.
Source values are user-entered or supplied by the original public examples.
HTML contains controls, not source values or expected choices. The
[recording](https://github.com/FluidInference/FluidAudio/blob/7f9eb92b0af8594c4e048a9e57f697340aacfa67/Examples/CuaS1FormsDemo/browser-demo.mp4)
shows patient, job and insurance forms: **100/100 original decisions** across
both models, with event-count, stale-observation and explicit-click checks.
Full actual contexts/candidates/actions are in [browser-validation.json](reports/browser-validation.json).
This is bounded local browser automation, not arbitrary desktop control or PDF extraction.

The expanded **release Swift** comparison uses all 50 initial controls, both
models resident, one warmup pass per model and ABBA with two full passes per
block (200 timed calls/model). All 400 choices match upstream labels. On the
M5 Pro / 24 GB / macOS 27.0 (26A428), original median/p95 is **0.912/0.933 ms**;
ANE gather is **0.961/0.984 ms**, about 5.4% slower by median. This timer includes
Swift encoding + Core ML + output decoding and excludes browser/rendering/animation.
See [swift-variant-comparison.json](reports/swift-variant-comparison.json) for
raw samples, exact model hashes, per-form statistics, and load/first-call costs.
The earlier compute-plan counts still apply to these unchanged artifacts;
no utilization, energy saving or held-out accuracy claim is made.

Reproduce from FluidAudio commit `7f9eb92b0af8594c4e048a9e57f697340aacfa67` (the example is
retained in history and is not part of the current library PR):

```bash
git worktree add --detach /tmp/cua-s1-browser-repro 7f9eb92b0af8594c4e048a9e57f697340aacfa67
cd /tmp/cua-s1-browser-repro
Examples/CuaS1FormsDemo/run.sh --browser
swift run --package-path Examples/CuaS1FormsDemo -c release CuaS1FormsDemo \
  --benchmark --report /absolute/path/to/variant-comparison.json \
  --hardware "Describe the measured Mac"
```

Both packages are fetched by pinned revisions and verified hashes, or supplied
with `--model /path/to/original.mlpackage --ane-model /path/to/ane-gather.mlpackage`.
The [demo README](https://github.com/FluidInference/FluidAudio/tree/7f9eb92b0af8594c4e048a9e57f697340aacfa67/Examples/CuaS1FormsDemo#matched-swift-benchmark)
also documents real-browser recording and its separate validation trace.