Instructions to use ridcl/paperwerk-vqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ridcl/paperwerk-vqa with Transformers:

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

pipe = pipeline("image-text-to-text", model="ridcl/paperwerk-vqa")
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("ridcl/paperwerk-vqa")
model = AutoModelForMultimodalLM.from_pretrained("ridcl/paperwerk-vqa")
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 ridcl/paperwerk-vqa with vLLM:

Install from pip and serve model

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

SGLang

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

Model Card for Model ID

VQA model that also supports bounding boxes.

Run via vLLM:

vllm serve ridcl/paperwerk-vqa \
    --max-model-len 32768 \
    --enable-auto-tool-choice \
    --tool-call-parser hermes \
    --tensor-parallel-size 2

Examples input:

{
    "role": "user",
    "content": [
        {
            "type": "image_url",
            "image_url": {
                "url": "data:image/jpeg;base64,..."
            },
        },
        {"type": "text", "text": "Extract values:\n- contract_date\n- names of the parties\n- Where is the company registered?"},
    ],
}

Example output (bbox format: [xmin, ymin, xmax, ymax], 0..1000 scale):

[
    {
        "query": "contract_date",
        "value": "1 April 2025",
        "box_2d": [466, 693, 544, 705],
        "index": 0,
    },
    {
        "query": "names of the parties",
        "value": "CoolCo B.V.",
        "box_2d": [151, 279, 257, 292],
        "index": 0,
    },
    {
        "query": "names of the parties",
        "value": "John Doe",
        "box_2d": [151, 403, 261, 415],
        "index": 0,
    },
    {
        "query": "names of the parties",
        "value": "Jane Doe",
        "box_2d": [151, 509, 268, 522],
        "index": 0,
    },
    {
        "query": "Where is the company registered?",
        "value": "Amsterdam",
        "box_2d": [164, 302, 281, 315],
        "index": 0,
    },
]

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Safetensors

Model size

4B params

Tensor type

BF16

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Qwen/Qwen3-VL-4B-Instruct

Finetuned

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