model_data_json/AskUI_PTA-1.json

{
    "model_id": "AskUI/PTA-1",
    "downloads": 10891,
    "tags": [
        "transformers",
        "safetensors",
        "florence2",
        "text-generation",
        "computer use",
        "image-text-to-text",
        "custom_code",
        "en",
        "base_model:microsoft/Florence-2-base",
        "base_model:finetune:microsoft/Florence-2-base",
        "license:mit",
        "autotrain_compatible",
        "region:us"
    ],
    "description": "--- library_name: transformers pipeline_tag: image-text-to-text tags: - computer use license: mit language: - en base_model: - microsoft/Florence-2-base --- # PTA-1: Controlling Computers with Small Models PTA (Prompt-to-Automation) is a vision language model for computer & phone automation, based on Florence-2. With only 270M parameters it outperforms much larger models in GUI text and element localization. This enables low-latency computer automation with local execution. ▶️ Try the demo at: AskUI/PTA-1 **Model Input:** Screenshot + description_of_target_element **Model Output:** BoundingBox for Target Element !image ## How to Get Started with the Model Use the code below to get started with the model. *Requirements:* torch, timm, einops, Pillow, transformers ## Evaluation **Note:** This is a first version of our evaluation, based on 999 samples (333 samples from each dataset). We are still running all models on the full test sets, and we are seeing ±5% deviations for a subset of the models we have already evaluated. | Model | Parameters | Mean | agentsea/wave-ui | AskUI/pta-text | ivelin/rico_refexp_combined | |--------------------------------------------|------------|--------|------------------|----------------|-----------------------------| | AskUI/PTA-1 | 0.27B | 79.98 | 90.69* | 76.28 | 72.97* | | anthropic.claude-3-5-sonnet-20241022-v2:0 | - | 70.37 | 82.28 | 83.18 | 45.65 | | agentsea/paligemma-3b-ft-waveui-896 | 3.29B | 57.76 | 70.57* | 67.87 | 34.83 | | Qwen/Qwen2-VL-7B-Instruct | 8.29B | 57.26 | 47.45 | 60.66 | 63.66 | | agentsea/paligemma-3b-ft-widgetcap-waveui-448 | 3.29B | 53.15 | 74.17* | 53.45 | 31.83 | | microsoft/Florence-2-base | 0.27B | 39.44 | 22.22 | 81.38 | 14.71 | | microsoft/Florence-2-large | 0.82B | 36.64 | 14.11 | 81.98 | 13.81 | | EasyOCR | - | 29.43 | 3.9 | 75.08 | 9.31 | | adept/fuyu-8b | 9.41B | 26.83 | 5.71 | 71.47 | 3.3 | | Qwen/Qwen2-VL-2B-Instruct | 2.21B | 23.32 | 17.12 | 26.13 | 26.73 | | Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4 | 0.90B | 18.92 | 10.81 | 22.82 | 23.12 | \\* Models is known to be trained on the train split of that dataset. The high benchmark scores for our model are partially due to data bias. Therefore, we expect users of the model to fine-tune it according to the data distributions of their use case. #### Metrics Click success rate is calculated as the number of clicks inside the target bounding box relative to all clicks. If a model predicts a target bounding box instead of a click coordinate, its center is used as its click prediction.",
    "model_explanation_gemini": "\"AskUI_PTA-1 is a 270M-parameter vision-language model for computer/phone automation, excelling in GUI text/element localization by generating bounding boxes from screenshots and target descriptions, enabling low-latency local execution.\"  \n\n**Features:**  \n- Small size (270M parameters)  \n- Image-text-to-text pipeline (screenshot + description → bounding box)  \n- Optimized for GUI text/element localization  \n- Low-latency local execution  \n- Out",
    "release_year": null,
    "parameter_count": null,
    "is_fine_tuned": true,
    "category": "Multimodal",
    "api_enhanced": true
}