---
license: apache-2.0
datasets:
- InternSVG/SAgoge
base_model:
- OpenGVLab/InternVL3-8B
---
<div align="center">
 <h1> InternSVG: Towards Unified SVG Tasks with Multimodal Large Language Models </h1>

<div align="center">
<a href='https://arxiv.org/abs/2510.11341'><img src='https://img.shields.io/badge/arXiv-2510.11341-b31b1b?logo=arXiv'></a> &nbsp;&nbsp;&nbsp;&nbsp;
<a href='https://hmwang2002.github.io/release/internsvg/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> &nbsp;&nbsp;&nbsp;&nbsp;
<a href="https://huggingface.co/datasets/InternSVG/SArena"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Benchmark%20-HF-orange"></a> &nbsp;&nbsp;&nbsp;&nbsp;
<a href="https://huggingface.co/datasets/InternSVG/SAgoge"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Dataset%20-HF-orange"></a> &nbsp;&nbsp;&nbsp;&nbsp;
<a href="https://huggingface.co/InternSVG/InternSVG-8B"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Model%20-HF-orange"></a>
</div>
</div>

## **🤖 InternSVG Model** 

The **InternSVG-8B** model is available at [Hugging Face](https://huggingface.co/InternSVG/InternSVG-8B). It is based on the InternVL3-8B model, incorporating SVG-specific tokens, and undergoes Supervised Fine-Tuning (SFT) under a two-stage training strategy using the massive SVG training samples from the SAgoge dataset. 

### Deploy

We recommend using [LMDeploy](https://github.com/InternLM/lmdeploy) for deployment. An example of launching a proxy server with 8 parallel workers (one per GPU) is provided below:

```bash
#!/bin/bash
model_path="MODEL_PATH"
model_name="InternSVG"

# proxy
lmdeploy serve proxy --server-name 0.0.0.0 --server-port 10010 --routing-strategy "min_expected_latency" &

worker_num=8
for ((i = 0; i < worker_num; i++)); do
    timestamp=$(date +"%Y-%m-%d_%H-%M-%S")
    CUDA_VISIBLE_DEVICES="${i}" lmdeploy serve api_server ${model_path} --proxy-url http://0.0.0.0:10010 \
        --model-name ${model_name} \
        --tp 1 \
        --max-batch-size 512 \
        --backend pytorch \
        --server-port $((10000 + i)) \
        --session-len 16384 \
        --chat-template "internvl2_5" \
        --log-level WARNING &>> ./logs/api_${model_name}_${timestamp}_${i}.out  &
    sleep 10s
done
```

### Train

If you need to train your own model, please follow these steps:

1. **Prepare the Dataset:** Download the **SAgoge** dataset. After that, update the paths for the SAgoge-related subdatasets in `LLaMA-Factory/data/dataset_info.json` to match your local file paths.
2. **Download InternVL3-8B:** Download the InternVL3-8B from [link](https://huggingface.co/OpenGVLab/InternVL3-8B-hf).
3. **Add Special Tokens:** Before training, you must add SVG-specific tokens to the base model. Run the `utils/add_token.py` script, which adds these special tokens to the original model weights and initializes their embeddings based on subwords.
4. **Start Training:** We provide example configuration scripts for the two-stage training process. You can find them at:
    - **Stage 1:** `LLaMA-Factory/examples/train_full/stage_1.yaml`
    - **Stage 2:** `LLaMA-Factory/examples/train_full/stage_2.yaml`

    Then use `llamafactory-cli train` to start training.

## 📖 Citation

```BibTex
@article{wang2025internsvg,
  title={InternSVG: Towards Unified SVG Tasks with Multimodal Large Language Models},
  author={Wang, Haomin and Yin, Jinhui and Wei, Qi and Zeng, Wenguang and Gu, Lixin and Ye, Shenglong and Gao, Zhangwei and Wang, Yaohui and Zhang, Yanting and Li, Yuanqi and others},
  journal={arXiv preprint arXiv:2510.11341},
  year={2025}
}
```