Instructions to use Allenda/VIEScore2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Allenda/VIEScore2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Allenda/VIEScore2")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Allenda/VIEScore2")
model = AutoModelForMultimodalLM.from_pretrained("Allenda/VIEScore2", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Allenda/VIEScore2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Allenda/VIEScore2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Allenda/VIEScore2",
		"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/Allenda/VIEScore2

SGLang

How to use Allenda/VIEScore2 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 "Allenda/VIEScore2" \
    --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": "Allenda/VIEScore2",
		"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 "Allenda/VIEScore2" \
        --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": "Allenda/VIEScore2",
		"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 Allenda/VIEScore2 with Docker Model Runner:
```
docker model run hf.co/Allenda/VIEScore2
```

VIEScore2 / README.md

Allenda

Update README.md

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---
license: apache-2.0
base_model: Qwen/Qwen3-VL-8B-Instruct
pipeline_tag: image-text-to-text
library_name: transformers
tags:
  - image-evaluation
  - image-quality-assessment
  - defect-localization
  - grounding
  - grpo
  - reinforcement-learning
---
# VIEScore2

A unified evaluator for generated and edited images. In a single
autoregressive pass the model emits:

- a **quality score** — dual-axis perceptual quality (`pq:`) and semantic
  consistency (`sc:`),
- a **defect grid** — sparse cells on a text-native 16×16 grid localizing
  problems, optionally split into `artifact:` / `misalign:` channels, and
- a **verifiable explanation**, rendered deterministically from the score and
  grid so the text can never contradict the spatial evidence.

Because every output element is a discrete text token set, predictions are
exactly checkable against ground truth. Training is supervised fine-tuning
followed by **GRPO with a verifiable cell-level F_β reward** on the defect
grid.
## Repository contents
**Naming convention:** `VIEScore2` denotes the full model after GRPO;
ablated variants are marked by what they lack, never by added suffixes.
| path | contents |
|---|---|
| `/` (root) | **VIEScore2** — the full post-GRPO checkpoint (drop-in `Qwen3VLForConditionalGeneration`) |
| `wo-grpo/` | **VIEScore2 (w/o GRPO)** — the SFT-stage LoRA adapter on `Qwen/Qwen3-VL-8B-Instruct` |
## Usage
```python
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
model = Qwen3VLForConditionalGeneration.from_pretrained(
    "Allenda/VIEScore2", dtype="bfloat16", device_map="auto")
processor = AutoProcessor.from_pretrained("Allenda/VIEScore2")
```
Evaluation prompts, the frozen protocol, the deterministic explanation
renderer, and all benchmark converters and baseline harnesses live in the
accompanying code release (see the paper).
## Evaluation
On a 1,300-example multi-source suite (RichHF, PAL4VST, EvalMuse,
ImagenWorld, COCO; frozen protocol v3.1): localization cell-F1 **0.506**,
problem-sample grid IoU **0.324**, overall-score SRCC **0.601**. After mapping
all methods to a shared 16×16 grid, VIEScore2 is the only evaluator in the
top two of cell-F1 or grid-IoU on all six external localization benchmarks
(RichHF, AbHuman, HAD, SynthScars, PAL4VST, SDG-30K). See the paper and code
repository for the full tables and protocol details.
## Citation
```
@misc{du2026viescore2unifiedimageevaluation,
      title={VIEScore2: Unified Image Evaluation with Spatially Grounded Explanations}, 
      author={Xianda Du and Max Ku and Weiming Ren and Zhi Rui Tam and Chunlin Ren and Ping Nie and Min-Hung Chen and Wenhu Chen},
      year={2026},
      eprint={2610.00994},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2610.00994}, 
}
```

	---
	license: apache-2.0
	base_model: Qwen/Qwen3-VL-8B-Instruct
	pipeline_tag: image-text-to-text
	library_name: transformers
	tags:
	- image-evaluation
	- image-quality-assessment
	- defect-localization
	- grounding
	- grpo
	- reinforcement-learning
	---

	# VIEScore2

	A unified evaluator for generated and edited images. In a single
	autoregressive pass the model emits:

	- a quality score — dual-axis perceptual quality (`pq:`) and semantic
	consistency (`sc:`),
	- a defect grid — sparse cells on a text-native 16×16 grid localizing
	problems, optionally split into `artifact:` / `misalign:` channels, and
	- a verifiable explanation, rendered deterministically from the score and
	grid so the text can never contradict the spatial evidence.

	Because every output element is a discrete text token set, predictions are
	exactly checkable against ground truth. Training is supervised fine-tuning
	followed by GRPO with a verifiable cell-level F_β reward on the defect
	grid.

	## Repository contents

	Naming convention: `VIEScore2` denotes the full model after GRPO;
	ablated variants are marked by what they lack, never by added suffixes.

	\| path \| contents \|
	\|---\|---\|
	\| `/` (root) \| VIEScore2 — the full post-GRPO checkpoint (drop-in `Qwen3VLForConditionalGeneration`) \|
	\| `wo-grpo/` \| VIEScore2 (w/o GRPO) — the SFT-stage LoRA adapter on `Qwen/Qwen3-VL-8B-Instruct` \|

	## Usage

	```python
	from transformers import AutoProcessor, Qwen3VLForConditionalGeneration

	model = Qwen3VLForConditionalGeneration.from_pretrained(
	"Allenda/VIEScore2", dtype="bfloat16", device_map="auto")
	processor = AutoProcessor.from_pretrained("Allenda/VIEScore2")
	```

	Evaluation prompts, the frozen protocol, the deterministic explanation
	renderer, and all benchmark converters and baseline harnesses live in the
	accompanying code release (see the paper).

	## Evaluation

	On a 1,300-example multi-source suite (RichHF, PAL4VST, EvalMuse,
	ImagenWorld, COCO; frozen protocol v3.1): localization cell-F1 0.506,
	problem-sample grid IoU 0.324, overall-score SRCC 0.601. After mapping
	all methods to a shared 16×16 grid, VIEScore2 is the only evaluator in the
	top two of cell-F1 or grid-IoU on all six external localization benchmarks
	(RichHF, AbHuman, HAD, SynthScars, PAL4VST, SDG-30K). See the paper and code
	repository for the full tables and protocol details.

	## Citation

	```
	@misc{du2026viescore2unifiedimageevaluation,
	title={VIEScore2: Unified Image Evaluation with Spatially Grounded Explanations},
	author={Xianda Du and Max Ku and Weiming Ren and Zhi Rui Tam and Chunlin Ren and Ping Nie and Min-Hung Chen and Wenhu Chen},
	year={2026},
	eprint={2610.00994},
	archivePrefix={arXiv},
	primaryClass={cs.CV},
	url={https://arxiv.org/abs/2610.00994},
	}
	```