Instructions to use utter-project/TowerVision-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use utter-project/TowerVision-2B with Transformers:

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

pipe = pipeline("image-text-to-text", model="utter-project/TowerVision-2B")
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 AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("utter-project/TowerVision-2B")
model = AutoModelForImageTextToText.from_pretrained("utter-project/TowerVision-2B")
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 = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use utter-project/TowerVision-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "utter-project/TowerVision-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "utter-project/TowerVision-2B",
		"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/utter-project/TowerVision-2B

SGLang

How to use utter-project/TowerVision-2B 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 "utter-project/TowerVision-2B" \
    --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": "utter-project/TowerVision-2B",
		"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 "utter-project/TowerVision-2B" \
        --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": "utter-project/TowerVision-2B",
		"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 utter-project/TowerVision-2B with Docker Model Runner:
```
docker model run hf.co/utter-project/TowerVision-2B
```

Guilherme Viveiros commited on Oct 28, 2025

Commit

b73737b

verified ·

1 Parent(s): 35c0c88

Update README.md

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@@ -324,12 +324,14 @@ TowerVision excels particularly in multimodal multilingual translation benchmark
 If you find TowerVision useful in your research, please consider citing the following paper:
 ```bibtex
-@article{towervision2025,
-  title={Understanding and Improving Multilinguality in Vision-Language Models},
-  author={[Authors to be added]},
-  journal={[Journal to be added]},
-  year={2025},
-  note={Paper in preparation}
 }
 ```

 If you find TowerVision useful in your research, please consider citing the following paper:
 ```bibtex
+@misc{viveiros2025towervisionundermstandingimprovingmultilinguality,
+      title={TowerVision: Understanding and Improving Multilinguality in Vision-Language Models},
+      author={André G. Viveiros and Patrick Fernandes and Saul Santos and Sonal Sannigrahi and Emmanouil Zaranis and Nuno M. Guerreiro and Amin Farajian and Pierre Colombo and Graham Neubig and André F. T. Martins},
+      year={2025},
+      eprint={2510.21849},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2510.21849},
 }
 ```