README.md

---
library_name: transformers
pipeline_tag: image-text-to-text
license: other
tags:
  - chart-vqa
  - visual-question-answering
  - qwen2.5-vl
  - multimodal
  - numerical-reasoning
---

# Nicesse-RARA-3B

Nicesse-RARA-3B is a complete 3B chart visual question answering model in the Qwen2.5-VL family. It is trained with **Reweighted Answer-Restricted Adaptation (RARA)**, which combines challenge-aware example exposure with answer-focused supervision for numerical and comparative chart reasoning. The repository contains the complete model checkpoint and the matching processor configuration for direct use with `transformers`.

## Model Details

| Item | Value |
|---|---|
| Model family | Qwen2.5-VL-3B |
| Parameter scale | 3B |
| Architecture | Vision-language generation model |
| Training objective | Challenge-aware sampling with answer-restricted supervision |
| Primary use | Chart visual question answering and numerical reasoning |
| Inputs | Chart image and natural-language question |
| Outputs | Short free-form answer |

## Benchmark Results

The table reports publicly available results on four chart VQA benchmarks. Scores are dataset-level accuracy in percent; higher is better. ChartQAPro additionally has a strict-audit score of 22.28.

| Method | ChartQA | PlotQA | EvoChart | ChartQAPro | Average |
|---|---:|---:|---:|---:|---:|
| Qwen2.5-VL-3B (direct prompting) | 82.00 | 80.50 | 48.72 | 25.70 | 59.23 |
| Qwen2.5-VL-3B (CoT) | 73.12 | 52.72 | 29.60 | 15.80 | 42.81 |
| ChartGemma | 76.44 | 33.28 | 36.96 | 10.93 | 39.40 |
| SketchVL-3B | 77.20 | 48.32 | 47.28 | 44.15 | 54.24 |
| Qwen2.5-VL-3B SFT | 83.08 | 74.18 | 46.08 | 23.56 | 56.73 |
| Qwen2.5-VL-3B DPO | 75.42 | 53.86 | 34.80 | 15.95 | 45.01 |
| Qwen2.5-VL-3B (A+F+L) | 76.72 | 56.22 | 38.88 | 17.55 | 47.34 |
| Qwen2.5-VL-3B (A+F+L+Tasks) | 81.80 | 76.24 | 51.68 | 27.66 | 59.35 |
| Chart-RVR-3B | **84.56** | 78.68 | 53.36 | 28.38 | 61.25 |
| Chart-RVR-3B-Hard | **85.76** | 77.90 | 54.24 | 28.64 | 61.64 |
| **Qwen2.5-VL + Nicesse-RARA-3B** | 83.76 | **89.42** | **54.32** | **37.94** | **66.36** |

The Nicesse-RARA-3B configuration was selected using a frozen development split. It was then evaluated once on four frozen benchmark manifests. The result is strongest on PlotQA among the listed comparable public 3B models; its ChartQAPro result follows the published-code-compatible evaluation protocol.

## Quick Start

```python
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration

model_id = "Junlaii/Nicesse-RARA-3B"
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_id, torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)

messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": "path/to/chart.png"},
        {"type": "text", "text": "What is the value of the blue bar in 2010?"},
    ],
}]
prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[prompt], images=["path/to/chart.png"], return_tensors="pt").to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=768)
print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0])
```

## Intended Use and Limitations

Nicesse-RARA-3B is intended for research on chart understanding, visual question answering, and numerical reasoning. It can make visual-reading, arithmetic, formatting, and ambiguous-question errors. Benchmark results do not establish reliability for high-stakes decisions; validate outputs independently in any downstream deployment.

## License and Attribution

This model is distributed under the included **Qwen Research License Agreement**. It is improved using Qwen. Please follow the license terms and cite the relevant Qwen2.5-VL and RARA work when appropriate.