Instructions to use ImDim/RotVL-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ImDim/RotVL-2B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="ImDim/RotVL-2B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ImDim/RotVL-2B")
model = AutoModelForMultimodalLM.from_pretrained("ImDim/RotVL-2B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ImDim/RotVL-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ImDim/RotVL-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ImDim/RotVL-2B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ImDim/RotVL-2B

SGLang

How to use ImDim/RotVL-2B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ImDim/RotVL-2B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ImDim/RotVL-2B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ImDim/RotVL-2B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ImDim/RotVL-2B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use ImDim/RotVL-2B with Docker Model Runner:
```
docker model run hf.co/ImDim/RotVL-2B
```

RotVL-2B / README.md

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	---
	library_name: transformers
	pipeline_tag: image-text-to-text
	base_model:
	- Qwen/Qwen3-VL-2B-Instruct
	- Qwen/Qwen3-VL-4B-Instruct
	- Qwen/Qwen3-VL-8B-Instruct
	tags:
	- vision-language
	- multimodal
	- android
	- gui
	- software-testing
	- rotation-bug
	- qwen3-vl
	- vllm
	---
	# RotVL
	RotVL is a rotation-aware vision-language model fine-tuned for cross-orientation state-equivalence checking and for detecting, classifying, and localizing GUI defects caused by screen rotation in Android applications. It accompanies the paper “RotDroid: Cross-Orientation State Equivalence Testing for Detecting GUI Rotation Bugs in Android Apps”, accepted at the 37th IEEE International Symposium on Software Reliability Engineering (ISSRE 2026).

	RotDroid supplies RotVL with two screenshots representing corresponding portrait and landscape states. RotVL determines whether a rotation-induced GUI defect is present and, when requested, returns the defective orientation, defect type, and bounding box.

	## Released checkpoints
	The three released checkpoints are:
	\| Checkpoint \| Base model \|
	\| ------------------------------------------------------ \| -------------------- \|
	\| [ImDim/RotVL-2B](https://huggingface.co/ImDim/RotVL-2B) \| Qwen3-VL-2B-Instruct \|
	\| [ImDim/RotVL-4B](https://huggingface.co/ImDim/RotVL-4B) \| Qwen3-VL-4B-Instruct \|
	\| [ImDim/RotVL-8B](https://huggingface.co/ImDim/RotVL-8B) \| Qwen3-VL-8B-Instruct \|

	Each RotVL checkpoint is obtained by fine-tuning the corresponding Qwen3-VL-Instruct model on the [RotBench dataset](https://huggingface.co/datasets/ImDim/RotBench). It accepts two Android GUI screenshots and a text instruction, and produces JSON output for bug detection or bug classification/localization.

	## Evaluation on RotBench
	The models are evaluated on RotBench test splits for bug detection, bug classification, defective-orientation identification, and bounding-box localization. Accuracy (Acc.), precision (Prec.), recall (Rec.), and F1 are percentages. For coordinate localization, a lower center-point distance is better, and an area ratio closer to 1 is better.

	### Bug detection (%)
	\| Model \| Acc. \| Prec. \| Rec. \| F1 \|
	\| ------------------ \| --------------: \| --------------: \| --------------: \| --------------: \|
	\| Qwen3-VL-2B \| 53.24 \| 62.82 \| 53.24 \| 42.48 \|
	\| Qwen3-VL-4B \| 61.47 \| 61.54 \| 61.47 \| 61.41 \|
	\| Qwen3-VL-8B \| 66.47 \| 67.65 \| 66.47 \| 65.90 \|
	\| Qwen3-VL-32B \| 58.82 \| 59.73 \| 58.82 \| 57.84 \|
	\| Qwen3-VL-235B \| 60.00 \| 60.72 \| 60.00 \| 59.32 \|
	\| GPT-5.2 \| 70.88 \| 71.01 \| 70.88 \| 70.84 \|
	\| RotVL-2B \| 71.76 \| 74.21 \| 71.76 \| 71.03 \|
	\| RotVL-4B \| 76.47 \| 76.50 \| 76.47 \| 76.46 \|
	\| RotVL-8B \| 85.29 \| 85.37 \| 85.29 \| 85.29 \|

	### Bug classification (%)
	\| Model \| Acc. \| Prec. \| Rec. \| F1 \|
	\| ------------------ \| --------------: \| --------------: \| --------------: \| --------------: \|
	\| Qwen3-VL-2B \| 20.59 \| 8.76 \| 20.59 \| 9.56 \|
	\| Qwen3-VL-4B \| 21.47 \| 20.54 \| 21.47 \| 13.03 \|
	\| Qwen3-VL-8B \| 30.59 \| 52.49 \| 30.59 \| 26.02 \|
	\| Qwen3-VL-32B \| 30.00 \| 65.02 \| 30.00 \| 25.89 \|
	\| Qwen3-VL-235B \| 37.06 \| 46.05 \| 37.06 \| 33.77 \|
	\| GPT-5.2 \| 49.71 \| 65.70 \| 49.71 \| 49.95 \|
	\| RotVL-2B \| 48.53 \| 59.51 \| 48.53 \| 42.97 \|
	\| RotVL-4B \| 44.41 \| 65.37 \| 44.41 \| 39.54 \|
	\| RotVL-8B \| 62.65 \| 66.71 \| 62.65 \| 62.68 \|

	### Bug localization: orientation (%)
	\| Model \| Acc. \| Prec. \| Rec. \| F1 \|
	\| ------------------ \| --------------: \| --------------: \| --------------: \| --------------: \|
	\| Qwen3-VL-2B \| 54.12 \| 54.41 \| 54.12 \| 53.34 \|
	\| Qwen3-VL-4B \| 52.06 \| 75.53 \| 52.06 \| 37.75 \|
	\| Qwen3-VL-8B \| 54.41 \| 69.03 \| 54.41 \| 43.58 \|
	\| Qwen3-VL-32B \| 53.82 \| 75.99 \| 53.82 \| 41.31 \|
	\| Qwen3-VL-235B \| 56.18 \| 74.48 \| 56.18 \| 46.10 \|
	\| GPT-5.2 \| 68.82 \| 70.61 \| 68.82 \| 68.13 \|
	\| RotVL-2B \| 57.35 \| 68.95 \| 57.35 \| 49.56 \|
	\| RotVL-4B \| 71.47 \| 77.38 \| 71.47 \| 69.84 \|
	\| RotVL-8B \| 80.29 \| 82.36 \| 80.29 \| 79.97 \|

	### Bug localization: coordinates
	\| Model \| Center-point distance (px) \| Area ratio \|
	\| ------------------ \| -------------------------: \| -------------: \|
	\| Qwen3-VL-2B \| 466 \| 73.29 \|
	\| Qwen3-VL-4B \| 499 \| 53.72 \|
	\| Qwen3-VL-8B \| 435 \| 38.45 \|
	\| Qwen3-VL-32B \| 478 \| 30.07 \|
	\| Qwen3-VL-235B \| 459 \| 45.06 \|
	\| GPT-5.2 \| 276 \| 9.76 \|
	\| RotVL-2B \| 237 \| 4.87 \|
	\| RotVL-4B \| 263 \| 16.62 \|
	\| RotVL-8B \| 198 \| 3.17 \|

	RotVL-8B achieves the best result in every evaluated dimension. Its improvements over the strongest baseline are statistically significant for bug detection, bug classification, bug localization error (`p < 0.001`).

	## Evaluation on natural bugs

	Bug detection generalization is also evaluated using 100 runtime cross-orientation screenshot pairs sampled from 44 real-world applications with developer-confirmed rotation bugs.

	\| Model \| Accuracy \| Precision \| Recall \| F1 \|
	\| ------------------ \| --------------: \| --------------: \| --------------: \| --------------: \|
	\| Qwen3-VL-8B \| 63.00 \| 70.22 \| 63.00 \| 66.41 \|
	\| GPT-5.2 \| 65.00 \| 59.21 \| 65.00 \| 61.97 \|
	\| RotVL-8B \| 70.00 \| 74.81 \| 70.00 \| 72.32 \|

	RotVL-8B performs best on this natural bug set, indicating that its gains are not limited to the synthetic bug patterns in RotBench.

	## Download
	Choose one of `2B`, `4B`, or `8B` and download the corresponding checkpoint with the Hugging Face CLI. The following example downloads RotVL-8B:
	```bash
	MODEL_SIZE=8B
	hf download "ImDim/RotVL-${MODEL_SIZE}" \
	--local-dir "RotVL-${MODEL_SIZE}"
	```

	## Serve with vLLM
	The tested model-server setup uses Linux, NVIDIA V100 GPUs, CUDA 12.2 or later, and vLLM 0.11.1. Start the selected checkpoint as an OpenAI-compatible service:
	```bash
	MODEL_SIZE=8B
	CUDA_VISIBLE_DEVICES=0 VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
	vllm serve "RotVL-${MODEL_SIZE}" \
	--served-model-name "RotVL-${MODEL_SIZE}" \
	--limit-mm-per-prompt '{"image": 2}' \
	--host 0.0.0.0 \
	--port 8000 \
	--dtype float16 \
	--api-key xxx \
	--max-model-len 32768 \
	--tensor-parallel-size 1
	```

	The value passed to `--served-model-name` must match the model name used by the client.

	## Output formats
	### Bug detection
	RotVL is prompted to return a strict JSON object:
	```json
	{"bug": true}
	```

	### Bug classification and localization
	For a defective pair, RotVL is prompted to return:
	```json
	{
	"type": "layout-overlap",
	"image": "portrait",
	"bbox_2d": [100, 200, 400, 500]
	}
	```
	- `type` is one of `layout-overlap`, `layout-clip`, `layout-miss`, `direction-mismatch`, and `state-loseinput`.
	- `image` is `portrait` or `landscape`.
	- `bbox_2d` is `[x_min, y_min, x_max, y_max]` in the defective image's pixel coordinates.

	## Intended uses
	RotVL is intended for:
	- use as the visual defect detector in RotDroid;
	- research on Android GUI rotation-bug detection;
	- evaluation on RotBench.

	## Use with RotDroid
	For integration details and instructions on running RotDroid, see the [RotDroid source repository](https://github.com/ImDiM/RotDroid).

	## Related resources
	- RotDroid source code: https://github.com/ImDiM/RotDroid
	- RotBench dataset: https://huggingface.co/datasets/ImDim/RotBench
	- Full artifact: https://doi.org/10.5281/zenodo.21897206

	## Citation
	```bibtex
	@inproceedings{qin2026rotdroid,
	title = {RotDroid: Cross-Orientation State Equivalence Testing for Detecting GUI Rotation Bugs in Android Apps},
	author = {Qin, Mengdi and Jiang, Bo},
	booktitle = {Proceedings of the 37th IEEE International Symposium on Software Reliability Engineering (ISSRE)},
	year = {2026}
	}
	```