---
license: other
tags:
- cua-lite
- gui
- sft
task_categories:
- image-text-to-text
configs:
- config_name: default
  data_files:
  - split: train
    path:
    - "*/*/train*parquet"
    - "*/*/train/*.parquet"
    - "*/*/train/*/*.parquet"
  - split: validation
    path:
    - "*/*/validation*parquet"
    - "*/*/validation/*.parquet"
    - "*/*/validation/*/*.parquet"
- config_name: desktop.use
  data_files:
  - split: train
    path:
    - "desktop/use/train*parquet"
    - "desktop/use/train/*.parquet"
    - "desktop/use/train/*/*.parquet"
  - split: validation
    path:
    - "desktop/use/validation*parquet"
    - "desktop/use/validation/*.parquet"
    - "desktop/use/validation/*/*.parquet"
---

# cua-lite/OpenCUA

cua-lite preprocessed version of xlangai/AgentNet (OpenCUA). Desktop `use` trajectories from Ubuntu and Windows/Mac environments with pyautogui-style actions converted to CUA-lite tool calls.

## Origin

- [https://huggingface.co/datasets/xlangai/AgentNet](https://huggingface.co/datasets/xlangai/AgentNet)

## Load via `datasets`

```python
from datasets import load_dataset

# entire dataset
ds = load_dataset("cua-lite/OpenCUA")

# just one (platform, task_type) cohort
ds = load_dataset("cua-lite/OpenCUA", "desktop.use")
```

After loading, parse `metadata` as JSON before filtering by `metadata_kind`,
`dims`, or `others.*`; every row carries a rich metadata object inside that JSON
string (see schema below). CUA rows use `dims == [platform, task_type]`.

## Schema

Published parquet columns:

| column | type | notes |
|---|---|---|
| `images` | list[Image] | embedded PNG/JPEG bytes; HF viewer renders thumbnails |
| `messages` | string (JSON array) | parse as JSON to OpenAI-style turns with `role`, structured `content`, nested `tool_calls`, and `role:"tool"` results |
| `metadata` | string (JSON object) | parse as JSON to fields `metadata_kind`, `dims`, `extra_tool_schemas`, CUA-only `valid_actions`, and `others` |
| `_folded` | string (JSON array, optional) | folded grounding/understanding rows only; authoritative per-instruction `messages` / `metadata` members |

Coordinate values in `messages` are normalized to `[0, 1000]` integers.
The JSON examples below show the decoded shape, not the raw string cell.

`metadata.extra_tool_schemas[*]` uses the nested Chat Completions function-tool
declaration shape:

```json
{
  "metadata_kind": "cua",
  "dims": ["desktop", "use"],
  "extra_tool_schemas": [
    {
      "type": "function",
      "function": {
        "name": "bash",
        "description": "Run a shell command.",
        "parameters": {
          "type": "object",
          "properties": {"cmd": {"type": "string"}},
          "required": ["cmd"]
        }
      }
    }
  ],
  "valid_actions": ["click", "type"],
  "others": {}
}
```

`messages[].tool_calls[*]` uses the matching nested invocation shape. Tool
results pair `tool_calls[].id` with `role:"tool"` `tool_call_id`:

```json
[
  {
    "role": "user",
    "content": [
      {"type": "image", "index": 0},
      {"type": "text", "text": "Click the OK button."}
    ]
  },
  {
    "role": "assistant",
    "tool_calls": [
      {
        "id": "call_0000",
        "type": "function",
        "function": {
          "name": "computer",
          "arguments": {
            "actions": [
              {"action": "click", "coordinate": [640, 400]},
              {"action": "type", "text": "hello"}
            ]
          }
        }
      }
    ]
  },
  {
    "role": "tool",
    "tool_call_id": "call_0000",
    "content": [
      {"type": "image", "index": 1},
      {"type": "text", "text": "clicked; typed"}
    ]
  }
]
```

**Image-dedup (`grounding.*` / `understanding` cohorts).** These cohorts are
single-image-per-row and many rows share the same screenshot, so to avoid
re-embedding identical image bytes once per instruction they are stored
*folded*: one row per unique screenshot (image embedded once), carrying an
extra **`_folded`** column — a JSON string with the authoritative list of
per-instruction members for that screenshot. Each member's `messages` and
`metadata` values are the same opaque JSON strings described above. The row's
top-level `messages` is a JSON string containing the members concatenated for
viewer convenience. `use` cohorts are not folded. **Use
`lite.data.hf.download` to consume this repo** — it unfolds automatically back
to one row per instruction; reading the parquet directly yields the folded form.

## Layout

```
<platform>/<task_type>/<split>/<variant>/shard-NNNNN-of-NNNNN.parquet
```

- `platform` ∈ {desktop, browser, mobile}
- `task_type` ∈ {understanding, grounding.action, grounding.point, grounding.bbox, use} — used verbatim as the dir component
- HF config names are `<platform>.<task_type>` by default (e.g. `mobile.grounding.action`) — UNLESS the dataset was staged with `--config-names`, which sets verbatim, explicitly-chosen config names (see the `configs:` block above for the authoritative list). The agent registry lookup key in code is `<agent>@<platform>@<task_type>` (e.g. `qwen3_vl@mobile@grounding.action`); only this user-facing token uses `.` between platform and task_type, because `@` triggers a 403 on the dataset-viewer's signed image URLs.
- HF split names stay `train` / `validation` (the `datasets` library blacklists `<>:/\|?*` in split names; everything else is fine in config_name)
- `validation` is an in-distribution held-out slice: no validation **sample** also appears in `train` — content-identical rows (same `images` + same `messages`, differing only in their ids) are co-located into one split, so upstream re-publishing one sample under two ids cannot straddle the split. It is *not* disjoint in **images**: one screenshot legitimately backs many distinct samples, and only whole samples are co-located, so the same picture can appear on both sides. `test` is reserved for out-of-distribution benchmark datasets

## Stats

| platform | task_type | variant | train | validation |
|---|---|---|---:|---:|
| desktop | use | ubuntu | 4,897 | 102 |
| desktop | use | win_mac | 16,970 | 367 |

## Local mirror & SFT export

For local workflows (SFT export, dedup, mixing across datasets), use
`lite.data.hf.download` to mirror this repo back to the canonical local
layout:

```
$CUA_LITE_DATASETS_ROOT/cua-lite/OpenCUA/
  images/<hash[:2]>/<hash>.<ext>                          # content-addressed image store
  <platform>/<task_type>/<split>[/<variant>].parquet      # rows reference images by relative path
```

Rows in the local parquet have `images: list[str]`; bytes are extracted to
the image store. `lite.train.export.export_sft` consumes the local
form directly with `--image-root=$CUA_LITE_DATASETS_ROOT`.

- Total unique images: **393,745**
- Image store size: **186.99 GB**

## Notes

split: content-identical rows co-located, then hash_split on metadata.others.id with val_frac=0.02, seed=42, val_cap=2000 per (metadata.dims[0], metadata.dims[1], variant) (an upstream split label, where the source ships one, wins over the hash). A cap-bound carve depends on source iteration order and these parameters do not reproduce it; content co-location may make the final physical validation row count differ from the cap.

## License & citation

See original dataset (xlangai/AgentNet).

See https://huggingface.co/datasets/xlangai/AgentNet