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
```

nielsr HF Staff commited on Oct 30, 2025

Commit

1ab2b9a

verified ·

1 Parent(s): b531ee2

Add pipeline tag and library name to model card

Browse files

This PR enhances the model card for VisCoder2-7B by adding key metadata:

- `pipeline_tag: image-text-to-text`: This helps users discover the model under multimodal generation tasks on the Hugging Face Hub, as it generates visualization code that produces images from text instructions.
- `library_name: transformers`: The `config.json` indicates compatibility with `transformers` (e.g., `"model_type": "qwen2"`, `"transformers_version": "4.51.3"`), enabling the automated inference widget and "How to use" code snippets on the Hub.

Please review these additions.

Files changed (1) hide show

README.md +5 -3

README.md CHANGED Viewed

@@ -1,13 +1,15 @@
 ---
-license: apache-2.0
 datasets:
 - TIGER-Lab/VisCode-Multi-679K
 language:
 - en
-base_model:
-- Qwen/Qwen2.5-Coder-7B-Instruct
 tags:
 - code
 ---
 # VisCoder2-7B

 ---
+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