Instructions to use opendatalab/ChartVerse-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use opendatalab/ChartVerse-Coder with Transformers:

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

pipe = pipeline("text-generation", model="opendatalab/ChartVerse-Coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("opendatalab/ChartVerse-Coder")
model = AutoModelForCausalLM.from_pretrained("opendatalab/ChartVerse-Coder")
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 opendatalab/ChartVerse-Coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "opendatalab/ChartVerse-Coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "opendatalab/ChartVerse-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/opendatalab/ChartVerse-Coder

SGLang

How to use opendatalab/ChartVerse-Coder 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 "opendatalab/ChartVerse-Coder" \
    --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": "opendatalab/ChartVerse-Coder",
		"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 "opendatalab/ChartVerse-Coder" \
        --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": "opendatalab/ChartVerse-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use opendatalab/ChartVerse-Coder with Docker Model Runner:
```
docker model run hf.co/opendatalab/ChartVerse-Coder
```

starriver030515 commited on Jan 21

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@@ -167,11 +167,14 @@ if code_match:
 ## 📖 Citation
 ```bibtex
-@article{chartverse2026,
-  title={ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch},
-  author={Anonymous Authors},
-  journal={Anonymous ACL Submission},
-  year={2026}
 }
 ```

 ## 📖 Citation
 ```bibtex
+@misc{liu2026chartversescalingchartreasoning,
+      title={ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch},
+      author={Zheng Liu and Honglin Lin and Chonghan Qin and Xiaoyang Wang and Xin Gao and Yu Li and Mengzhang Cai and Yun Zhu and Zhanping Zhong and Qizhi Pei and Zhuoshi Pan and Xiaoran Shang and Bin Cui and Conghui He and Wentao Zhang and Lijun Wu},
+      year={2026},
+      eprint={2601.13606},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2601.13606},
 }
 ```