Update README.md

README.md

@@ -1,5 +1,9 @@
 ---
 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>
@@ -13,6 +17,58 @@ license: apache-2.0
 </div>
 </div>
 
-## 🤖 InternSVG-8B
+## **🤖 InternSVG Model** 
 
-A unified multimodal large language model (MLLM) for SVG understanding, editing, and generation.
+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}
+}
+```