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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](https://huggingface.co/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**
```python
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.
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