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Model: XunmeiLiu/VFIG-4B Source: Original Platform
2026-06-13 01:28:18 +08:00
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+---
+license: cc-by-4.0
+language:
+- en
+base_model: Qwen/Qwen3-VL-4B-Instruct
+library_name: transformers
+tags:
+- svg
+- vision-language
+- reinforcement-learning
+- grpo
+- figure-understanding
+- vector-graphics
+---
+
+# VFIG-4B
+
+This repository contains the merged RL-trained checkpoint for **VFIG-4B**, a 4B vision-language model for converting complex scientific and technical figures into clean, editable SVG code.
+
+- 📄 [Paper](https://arxiv.org/abs/2603.24575)
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| Base Model | Qwen/Qwen3-VL-4B-Instruct |
+| Training | SFT + GRPO-based RL with rendering-aware visual rewards |
+| Architecture | LoRA on language model; vision encoder and projector frozen |
+| Parameters | 4B |
+| Precision | BF16 |
+
+## Intended Use
+
+Given an input image of a scientific or technical figure, the model generates SVG code that reconstructs the figure as a scalable, editable vector graphic.
+
+## Quick Start
+
+```bash
+pip install transformers torch accelerate pillow
+```
+
+```python
+import torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForImageTextToText
+
+model_name = "XunmeiLiu/VFIG-4B"
+
+processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForImageTextToText.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="cuda",
+    trust_remote_code=True,
+)
+model.eval()
+
+def figure_to_svg(image_path: str) -> str:
+    img = Image.open(image_path).convert("RGB")
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image"},
+                {"type": "text", "text": "Convert this figure into valid SVG code."},
+            ],
+        }
+    ]
+
+    chat_input = processor.tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    inputs = processor(text=[chat_input], images=[img], return_tensors="pt").to("cuda")
+
+    with torch.no_grad():
+        output_ids = model.generate(**inputs, max_new_tokens=8192, do_sample=False)
+
+    decoded = processor.tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    if "<svg" in decoded:
+        decoded = decoded[decoded.find("<svg"):]
+    if "</svg>" in decoded:
+        decoded = decoded[: decoded.find("</svg>") + len("</svg>")]
+    return decoded.strip()
+
+# Download the example image
+import urllib.request
+urllib.request.urlretrieve(
+    "https://huggingface.co/XunmeiLiu/VFIG-4B/resolve/main/simple_diagram.png",
+    "simple_diagram.png"
+)
+
+svg_code = figure_to_svg("simple_diagram.png")
+print(svg_code)
+```
+
+## License
+
+This repository uses the `cc-by-4.0` license. Please also ensure that redistribution is compatible with the license of the underlying base model.
+
+## Citation
+
+If you use VFIG-4B in your research, please cite:
+
+```bibtex
+@misc{he2026vfigvectorizingcomplexfigures,
+      title={VFIG: Vectorizing Complex Figures in SVG with Vision-Language Models}, 
+      author={Qijia He and Xunmei Liu and Hammaad Memon and Ziang Li and Zixian Ma and Jaemin Cho and Jason Ren and Daniel S Weld and Ranjay Krishna},
+      year={2026},
+      eprint={2603.24575},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2603.24575}, 
+}