Instructions to use Surpem/Supertron-VL-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Surpem/Supertron-VL-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Surpem/Supertron-VL-4B")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Surpem/Supertron-VL-4B")
model = AutoModelForImageTextToText.from_pretrained("Surpem/Supertron-VL-4B")
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

vLLM

How to use Surpem/Supertron-VL-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Surpem/Supertron-VL-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Surpem/Supertron-VL-4B",
		"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/Surpem/Supertron-VL-4B

SGLang

How to use Surpem/Supertron-VL-4B 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 "Surpem/Supertron-VL-4B" \
    --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": "Surpem/Supertron-VL-4B",
		"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 "Surpem/Supertron-VL-4B" \
        --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": "Surpem/Supertron-VL-4B",
		"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 Surpem/Supertron-VL-4B with Docker Model Runner:
```
docker model run hf.co/Surpem/Supertron-VL-4B
```

Supertron-VL-4B / README.md

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metadata

license: apache-2.0
base_model:
  - Qwen/Qwen3-VL-4B-Thinking
pipeline_tag: image-text-to-text
library_name: transformers
metrics:
  - accuracy
tags:
  - vision-language
  - chart-question-answering
  - multimodal
  - pytorch
model-index:
  - name: Supertron-VL-4B
    results:
      - task:
          type: image-text-to-text
          name: Chart Question Answering
        dataset:
          name: ChartQA
          type: HuggingFaceM4/ChartQA
          split: test
        metrics:
          - name: ChartQA relaxed accuracy
            type: accuracy
            value: 0.7891
          - name: Exact match
            type: accuracy
            value: 0.7109

Supertron-VL-4B: A Chart-Focused Vision-Language Model

Model Description

Supertron-VL-4B is a vision-language model fine-tuned from Qwen/Qwen3-VL-4B-Thinking for chart understanding and chart question answering. It reads chart images, extracts values, compares visual elements, and answers concise questions about plotted data.

Developed by: Surpem
Model type: Vision-Language Model
Architecture: Qwen3-VL dense multimodal transformer, 4B class
Fine-tuned from: Qwen/Qwen3-VL-4B-Thinking
License: Apache 2.0

Evaluation

Local Modal H100 benchmark using the Hugging Face transformers image-text-to-text pipeline:

Benchmark	Split	Samples	Exact Accuracy	Relaxed ChartQA Accuracy
ChartQA	test	256	0.7109	0.7891

Note: This is an offline local benchmark, not an official Hugging Face leaderboard verification.

Get Started

from transformers import AutoProcessor, AutoModelForImageTextToText
from PIL import Image
import torch

model_id = "Surpem/Supertron-VL-4B"

processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

image = Image.open("chart.png").convert("RGB")
question = "What is the highest value shown in the chart?"
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {
                "type": "text",
                "text": (
                    "Read the chart image and answer the question concisely. "
                    "Return only the final answer, without chain-of-thought.\n"
                    f"Question: {question}"
                ),
            },
        ],
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], padding=True, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=48, do_sample=False)
generated = outputs[:, inputs["input_ids"].shape[1]:]
print(processor.batch_decode(generated, skip_special_tokens=True)[0].strip())

Limitations

Supertron-VL-4B is specialized for chart question answering. It may make mistakes on crowded charts, ambiguous labels, color-only questions, arithmetic-heavy questions, or charts with very small text.