Instructions to use TIGER-Lab/VisCoder2-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TIGER-Lab/VisCoder2-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="TIGER-Lab/VisCoder2-7B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TIGER-Lab/VisCoder2-7B")
model = AutoModelForCausalLM.from_pretrained("TIGER-Lab/VisCoder2-7B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TIGER-Lab/VisCoder2-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TIGER-Lab/VisCoder2-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/VisCoder2-7B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/TIGER-Lab/VisCoder2-7B

SGLang

How to use TIGER-Lab/VisCoder2-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TIGER-Lab/VisCoder2-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/VisCoder2-7B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TIGER-Lab/VisCoder2-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/VisCoder2-7B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use TIGER-Lab/VisCoder2-7B with Docker Model Runner:
```
docker model run hf.co/TIGER-Lab/VisCoder2-7B
```

VisCoder2-7B / README.md

yuanshengni

Update README.md

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	---
	base_model:
	- Qwen/Qwen2.5-Coder-7B-Instruct
	datasets:
	- TIGER-Lab/VisCode-Multi-679K
	language:
	- en
	license: apache-2.0
	tags:
	- code
	pipeline_tag: image-text-to-text
	library_name: transformers
	---

	# VisCoder2-7B

	[🏠 Project Page](https://tiger-ai-lab.github.io/VisCoder2) \| [📖 Paper](https://arxiv.org/abs/2510.23642) \| [💻 GitHub](https://github.com/TIGER-AI-Lab/VisCoder2) \| [🤗 VisCode2](https://hf.co/collections/TIGER-Lab/viscoder2)

	VisCoder2-7B is a lightweight multi-language visualization coding model trained for executable code generation, rendering, and iterative self-debugging.

	---

	## 🧠 Model Description

	VisCoder2-7B is trained on the VisCode-Multi-679K dataset, a large-scale instruction-tuning dataset for executable visualization tasks across 12 programming language. It addresses a core challenge in multi-language visualization: generating code that not only executes successfully but also produces semantically consistent visual outputs by aligning natural-language instructions and rendering results.

	---

	## 📊 Main Results on VisPlotBench

	We evaluate VisCoder2-7B on [VisPlotBench](https://huggingface.co/datasets/TIGER-Lab/VisPlotBench), which includes 888 executable visualization tasks spanning 8 languages, supporting both standard generation and multi-turn self-debugging.

	![main_results](https://cdn-uploads.huggingface.co/production/uploads/64de37ee5e192985054be575/DRR3Y5vVS-KbniGJ3wmTi.png)

	> VisCoder2-7B shows consistent performance across multiple languages and achieves notable improvements under the multi-round self-debug setting.
	---

	## 📁 Training Details

	- Base model: Qwen2.5-Coder-7B-Instruct
	- Framework: [ms-swift](https://github.com/modelscope/swift)
	- Tuning method: Full-parameter supervised fine-tuning (SFT)
	- Dataset: [VisCode-Multi-679K](https://huggingface.co/datasets/TIGER-Lab/VisCode-Multi-679K)

	---

	## 📖 Citation

	If you use VisCoder2-7B or related datasets in your research, please cite:

	```bibtex
	@article{ni2025viscoder2,
	title={VisCoder2: Building Multi-Language Visualization Coding Agents},
	author={Ni, Yuansheng and Cai, Songcheng and Chen, Xiangchao and Liang, Jiarong and Lyu, Zhiheng and Deng, Jiaqi and Zou, Kai and Nie, Ping and Yuan, Fei and Yue, Xiang and others},
	journal={arXiv preprint arXiv:2510.23642},
	year={2025}
	}

	@article{ni2025viscoder,
	title={VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation},
	author={Ni, Yuansheng and Nie, Ping and Zou, Kai and Yue, Xiang and Chen, Wenhu},
	journal={arXiv preprint arXiv:2506.03930},
	year={2025}
	}
	```

	For evaluation scripts and more information, see our [GitHub repository](https://github.com/TIGER-AI-Lab/VisCoder2).