---
base_model: Qwen/Qwen2.5-VL-3B-Instruct
library_name: transformers
model_name: Chart-RVR-3B
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---


# Model Card for Chart-RVR-3B

We present the first RL-trained chart-specific explainable model with SOTA performance on OOD datasets.
The model is trained to predict chart type, data table, CoT reasoning and the final answer.

# Chart-RVR Inference Demo

This script demonstrates how to use the [`sanchit97/chart-rvr-3b`](https://huggingface.co/sanchit97/chart-rvr-3b) model for chart-based reasoning using a vision-language interface. It loads a chart image from a URL, prompts the model with a question, and extracts the structured reasoning and final answer.

## 🧠 System Prompt

The assistant is instructed to return a structured response using `<think>` and `<answer>` tags. Inside `<think>`, it outputs the chart type, the data table in JSON, and reasoning steps.

## 🧪 Inference Code

```python
from transformers import AutoProcessor, AutoModelForVision2Seq
from PIL import Image
import requests
from io import BytesIO
import torch
from qwen_vl_utils import process_vision_info  # helper from Qwen repo

# Load processor and model
processor = AutoProcessor.from_pretrained("sanchit97/chart-rvr-3b")
model = AutoModelForVision2Seq.from_pretrained(
    "sanchit97/chart-rvr-3b", device_map="auto", torch_dtype=torch.bfloat16
)

# Define the structured system prompt
SYSTEM_PROMPT = """
You are a vision-language assistant. You are given a chart image and a query about the chart. 
Think step-by-step about how to answer the query based on the chart image and then provide the final answer.

### Output format
Respond **with exactly two blocks in order and nothing else**:
<think>
First output the type of chart in <type>, \
then output the underlying data table and finally, \ 
think step-by-step about how to answer the query based on the chart image \
and then provide the final answer.
<type>
Type of chart - one word from line, bar, stacked bar, pie, histogram, scatterplot, area, stacked area, bubble, treemap.
</type>
Next output the data table in the <table></table> tags
<table>
json table - for the chart image, output only a JSON object with: "columns": list of column headers, "rows": list-of-lists, one per data row
No prose, no comments.
1. Respond with **only** a JSON object
2. The JSON must use exactly this schema:
    {
        "columns": [...],
        "rows": [[...], [...],..., [...]]
    }
3. Do NOT output HTML, Markdown, or commentary. Any deviation gets zero reward.
</table>
Provide your reasoning here in steps:
<step-1>: Provide a description of reasoning
<step-2>: Gather ALL the appropriate data from the chart
<step-3>: Break down the query into smaller parts and verify each part with the data
...
<step-n>: Do the final calculation or reasoning to derive the answer
</think>
<answer>
Final answer on a single line
</answer>
"""

# Chart image from URL
image_url = "https://mathmonks.com/wp-content/uploads/2023/01/Parts-Bar-Graph.jpg"
response = requests.get(image_url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Query about the chart
prompt = "What is the average of all the bars in the chart?"

# Build multimodal chat input
messages = [
    {
        "role": "system",
        "content": SYSTEM_PROMPT
    },
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": prompt},
        ],
    },
]

# Format text and vision input
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)

inputs = processor(
    text=text,
    images=[image_inputs],
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
).to(model.device)

# Generate output
generated_ids = model.generate(**inputs, max_new_tokens=1024)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]

print("Generated Output: ", output)
print("Answer: ", output.split("<answer>")[-1].split("</answer>")[0].strip())
```




## Benchmark numbers

Some Raw numbers (more numbers+paper coming soon!):
1. ChartQA: 84.56
2. PlotQA: 78.68
3. ChartFC: 77.62 
4. EvoChart: 53.36
5. ChartQAPro: 28.38 


This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).

## Training procedure

<!-- [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/chartrl/chartrl/runs/nzaj1tyg)  -->


This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).

### Framework versions

- TRL: 0.20.0.dev0
- Transformers: 4.53.2
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1

## Citations

Cite GRPO as:

```bibtex
@article{zhihong2024deepseekmath,
    title        = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
    author       = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
    year         = 2024,
    eprint       = {arXiv:2402.03300},
}

```

Cite TRL as:
    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```