Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-VL-4B-Instruct
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- chart
+- reasoning
+- vision-language
+- multimodal
+- chart-understanding
+- VLM
+datasets:
+- opendatalab/ChartVerse-SFT-600K
+- opendatalab/ChartVerse-RL-40K
+---
+
+**ChartVerse-4B** is an efficient Vision Language Model (VLM) specialized for complex chart reasoning, developed as part of the **[opendatalab/ChartVerse](https://huggingface.co/collections/opendatalab/chartverse)** project. For more details about our method, datasets, and full model series, please visit our [GitHub Repository](https://github.com/starriver030515/ChartVerse) and [Project Page](https://chartverse.github.io).
+
+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
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/chartverse/chartverse.github.io/main/static/images/overall_result.png" width="100%" alt="Overall Performance Comparison">
+</div>
+
+### SFT vs RL Performance
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/chartverse/chartverse.github.io/main/static/images/training_phases.png" width="100%" alt="Training Phases Performance">
+</div>
+
+## 📚 Training Data
+
+### [ChartVerse-SFT-600K](https://huggingface.co/datasets/opendatalab/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](https://huggingface.co/datasets/opendatalab/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
+@article{chartverse2026,
+  title={ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch},
+  author={Anonymous Authors},
+  journal={Anonymous ACL Submission},
+  year={2026}
+}
+```
+
+## 📄 License
+
+This model is released under the Apache 2.0 License.
+
+## 🙏 Acknowledgements
+
+- Base model: [Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct)
+- Training frameworks: [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory), [veRL](https://github.com/volcengine/verl)
+- Evaluation: [VLMEvalKit](https://github.com/open-compass/VLMEvalKit)