---
license: mit
tags:
- onnx
- computer-use
- form-filling
- webgpu
- wasm
- cua
base_model: cua-ai/cua-s1-forms
---

# CUA-S1 Forms ONNX

ONNX conversion of the original
[`cua-ai/cua-s1-forms`](https://huggingface.co/cua-ai/cua-s1-forms)
model for local and browser inference experiments.

Original project:

https://github.com/trycua/cua/tree/main/libs/cua-s1

This repository only provides a deployment-format conversion of the
original trained checkpoint. The architecture, weights, training,
evaluation, and research belong to the original CUA-S1 project.

## Why ONNX?

CUA-S1 Forms is a small specialist model for form-oriented computer-use
tasks.

Instead of generating text token-by-token, it scores a bounded set of
candidate actions in a single forward pass.

This makes it suitable for lightweight local inference using:

- ONNX Runtime
- ONNX Runtime Web
- WebAssembly
- WebGPU

No server-side inference is required.

## Model

The original model uses:

- byte-level input encoding
- 2-layer Transformer encoder
- width 128
- 4 attention heads
- option-attention scoring
- ~706K parameters

Please refer to the original model card for the authoritative architecture,
training details, evaluation results, limitations, and safety guidance:

https://huggingface.co/cua-ai/cua-s1-forms

## Files

```text
cua-s1-forms.onnx
cua-s1-forms-web.json
```

## Input contract

```text
context_ids
int64 [B, 224]

context_mask
bool [B, 224]

option_ids
int64 [B, N, 96]

option_token_mask
bool [B, N, 96]

option_mask
bool [B, N]
```

Output:

```text
logits
float32 [B, N]
```

Where:

```text
B = batch size
N = candidate options
```

## Byte encoding

CUA-S1 uses UTF-8 bytes directly:

```python
byte_id = byte + 1
```

`0` is reserved for padding.

This means browser implementations can use JavaScript `TextEncoder`
without requiring a tokenizer model.

## Python example

```python
import onnxruntime as ort

session = ort.InferenceSession(
    "cua-s1-forms.onnx",
    providers=["CPUExecutionProvider"]
)

logits = session.run(
    ["logits"],
    {
        "context_ids": context_ids,
        "context_mask": context_mask,
        "option_ids": option_ids,
        "option_token_mask": option_token_mask,
        "option_mask": option_mask,
    }
)[0]
```

## Preliminary CPU result

Google Colab, ONNX Runtime CPU, batch 1, 8 candidate options:

```text
Median latency      15.0 ms
Mean latency        16.8 ms
P95                 24.3 ms
Throughput          ~60 decisions/sec
```

These are deployment measurements for this ONNX conversion, not official
CUA-S1 benchmark results.

Browser WASM and WebGPU tests will be added separately.

## Intended browser deployment

```text
Browser
   |
   v
TextEncoder
   |
   v
ONNX Runtime Web
   |
   +--> WASM
   |
   +--> WebGPU
   |
   v
CUA-S1 Forms
   |
   v
Action scores
```

## Limitations

All limitations of the original model still apply.

CUA-S1 Forms is a specialist form-oriented model, not a general-purpose
computer-use agent.

Refer to the original model card for detailed scope, limitations, evaluation,
and safety guidance.

## Attribution

Original model:

https://huggingface.co/cua-ai/cua-s1-forms

Original source:

https://github.com/trycua/cua/tree/main/libs/cua-s1

ONNX conversion:

https://huggingface.co/yasserrmd/cua-s1-forms-onnx

Converted for ONNX / browser deployment experiments by Mohamed Yasser.

This repository is not an official CUA-AI release.

## License

MIT, following the original model repository.