Name: ChartVerse-4B
Rating: 2.5 (18 reviews)

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ChartVerse-4B is an efficient Vision Language Model (VLM) specialized for complex chart reasoning, developed as part of the opendatalab/ChartVerse project. For more details about our method, datasets, and full model series, please visit our Project Page.

A key highlight is that ChartVerse-4B significantly outperforms Qwen3-VL-8B-Thinking (60.0%) despite using only half the parameters, demonstrating that data quality triumphs over model scale.

🔥 Highlights

Data Quality > Model Scale: 4B parameters achieving 61.9% average score, surpassing Qwen3-VL-8B-Thinking (60.0%)
Efficient Performance: Delivers 8B-level performance with 4B parameters
High-Quality Training: Trained on ChartVerse-SFT-600K and ChartVerse-RL-40K with rigorous truth-anchored QA synthesis
Strong Reasoning: Equipped with Chain-of-Thought reasoning for complex multi-step chart analysis

📊 Model Performance

Overall Results

SFT vs RL Performance

📚 Training Data

ChartVerse-SFT-600K

412K unique high-complexity charts
603K QA pairs with 3.9B tokens of CoT reasoning
Rollout Posterior Entropy: 0.44 (highest among all datasets)
Truth-anchored answer verification via code execution

ChartVerse-RL-40K

40K highest-difficulty samples
Filtered by failure rate: 0 < r(Q) < 1
Ensures "hard but solvable" training signal

🏋️ Training Details

Supervised Fine-Tuning (SFT):

Framework: LLaMA-Factory
Dataset: ChartVerse-SFT-600K
Learning rate: 1.0 × 10⁻⁵
Global batch size: 128
Context length: 22,000 tokens

Reinforcement Learning (RL):

Framework: veRL
Dataset: ChartVerse-RL-40K
Algorithm: GSPO
Learning rate: 1.0 × 10⁻⁶
Rollout samples: 16 per prompt

🚀 Quick Start

python

from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
from PIL import Image

# 1. Load Model
model_path = "opendatalab/ChartVerse-4B"
model = Qwen3VLForConditionalGeneration.from_pretrained(
    model_path, torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_path)

# 2. Prepare Input
image_path = "path/to/your/chart.png"
query = "Which region demonstrates the greatest proportional variation in annual revenue compared to its typical revenue level?"

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image_path},
            {"type": "text", "text": query},
        ],
    }
]

# 3. Inference
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,
    padding=True,
    return_tensors="pt",
).to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=16384)
output_text = processor.batch_decode(
    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text[0])

📖 Citation

bibtex

@misc{liu2026chartversescalingchartreasoning,
      title={ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch}, 
      author={Zheng Liu and Honglin Lin and Chonghan Qin and Xiaoyang Wang and Xin Gao and Yu Li and Mengzhang Cai and Yun Zhu and Zhanping Zhong and Qizhi Pei and Zhuoshi Pan and Xiaoran Shang and Bin Cui and Conghui He and Wentao Zhang and Lijun Wu},
      year={2026},
      eprint={2601.13606},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2601.13606}, 
}

📄 License

This model is released under the Apache 2.0 License.

🙏 Acknowledgements

Base model: Qwen3-VL-4B-Instruct
Training frameworks: LLaMA-Factory, veRL
Evaluation: VLMEvalKit