Model Card for Chart-RVR-HARD-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. Note this model is trained on curated chart data on reasoning.

Code to train this model is available here Dataset is available here

Chart-RVR Inference Demo

This script demonstrates how to use the sanchit97/chart-rvr-hard-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

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-hard-3b")
model = AutoModelForVision2Seq.from_pretrained(
    "sanchit97/chart-rvr-hard-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: 85.76
2. PlotQA: 77.9
3. ChartFC: 80.07
4. EvoChart: 54.24
5. ChartQAPro: 28.64

This model is a fine-tuned version of Qwen/Qwen2.5-VL-3B-Instruct. It has been trained using TRL.

Training procedure

This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

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:

@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:

@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}}
}