Instructions to use TIGER-Lab/VisCoder2-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TIGER-Lab/VisCoder2-3B with Transformers:

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

pipe = pipeline("text-generation", model="TIGER-Lab/VisCoder2-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TIGER-Lab/VisCoder2-3B")
model = AutoModelForCausalLM.from_pretrained("TIGER-Lab/VisCoder2-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TIGER-Lab/VisCoder2-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TIGER-Lab/VisCoder2-3B"
# 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/VisCoder2-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TIGER-Lab/VisCoder2-3B

SGLang

How to use TIGER-Lab/VisCoder2-3B 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/VisCoder2-3B" \
    --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/VisCoder2-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/VisCoder2-3B" \
        --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/VisCoder2-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TIGER-Lab/VisCoder2-3B with Docker Model Runner:
```
docker model run hf.co/TIGER-Lab/VisCoder2-3B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

VisCoder2-3B

🏠 Project Page | 📖 Paper | 💻 GitHub | 🤗 VisCode2

VisCoder2-3B is a lightweight multi-language visualization coding model trained for executable code generation, rendering, and iterative self-debugging.

🧠 Model Description

VisCoder2-3B 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 VisCoder2-3B on VisPlotBench, which includes 888 executable visualization tasks spanning 8 languages, supporting both standard generation and multi-turn self-debugging.

VisCoder2-3B shows consistent performance across multiple languages and achieves notable improvements under the multi-round self-debug setting.

📁 Training Details

Base model: Qwen2.5-Coder-3B-Instruct
Framework: ms-swift
Tuning method: Full-parameter supervised fine-tuning (SFT)
Dataset: VisCode-Multi-679K

📖 Citation

If you use VisCoder2-3B or related datasets in your research, please cite:

@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.