utter-project/TowerVision-2B

Model Card for TowerVision

TowerVision is a family of open-source multilingual vision-language models with strong capabilities optimized for a variety of vision-language use cases, including image captioning, visual understanding, summarization, question answering, and more. TowerVision excels particularly in multimodal multilingual translation benchmarks and culturally-aware tasks, demonstrating exceptional performance across 20 languages and dialects.

This model card covers the TowerVision family, including the 2B and 9B parameter versions, both in their instruct-tuned (it) and pretrained (pt) variants, with the latter not undergoing instruction tuning.

• Model Family: TowerVision (2B, 9B variants)
• Context length: 8192 tokens
• Languages: 20+ languages including European, Asian, and other language families

🌟 Try TowerVision: Project Page | Code Repository

Available Models

Model	Parameters	HF Link
TowerVision-2B	2B	🤗 utter-project/TowerVision-2B
TowerVision-2B-pt	2B	🤗 utter-project/TowerVision-2B-pt
TowerVision-9B	9B	🤗 utter-project/TowerVision-9B
TowerVision-9B-pt	9B	🤗 utter-project/TowerVision-9B-pt

How to Use TowerVision

When using the model, make sure your prompt is formated correctly! Also, we recommend using bfloat16 rather than fp32/16

Quick Start with Transformers

Click to expand/collapse code

from transformers import (
    LlavaNextProcessor,
    LlavaNextForConditionalGeneration
)
import requests
from PIL import Image

model_id = "utter-project/TowerVision-2B"  # or any other variant

def prepare_prompt(query):
    conversation = [
        {
            "role": "user", 
            "content": f"<image>\n{query}"
        }
    ]
    
    # Format message with the towervision chat template
    prompt = processor.apply_chat_template(
        conversation, 
        tokenize=False,
        add_generation_prompt=True
    )
    
    return prompt

# we recommend using "bfloat16" as torch_dtype
kwargs = {
    "torch_dtype": "bfloat16",
    "device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)

# img url
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)

# Multilingual prompts - TowerVision supports 20+ languages!
prompt = prepare_prompt("Is this person really big, or is this building just super small?")

# Prepare inputs
inputs = processor(
    text=prompt, images=image, return_tensors="pt"
).to(model.device)

# Generate response ids
gen_tokens = model.generate(**inputs, max_new_tokens=512)
# Decode response
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Batch Inference with Transformers

For processing multiple images and prompts simultaneously:

Click to expand/collapse code

def prepare_prompts(queries):
    prompts = []
    for query in queries:
        conversation = [
            {
                "role": "user", 
                "content": f"<image>\n{query}"
            }
        ]
        
        # Format message with the towervision chat template
        prompt = processor.apply_chat_template(
            conversation, 
            tokenize=False,
            add_generation_prompt=True
        )
        prompts.append(prompt)
    return prompts

# we recommend using "bfloat16" as torch_dtype
kwargs = {
    "torch_dtype": "bfloat16",
    "device_map": "auto",
}
processor = LlavaNextProcessor.from_pretrained(model_id)
model = LlavaNextForConditionalGeneration.from_pretrained(model_id, **kwargs)

# Sample images and queries for batch processing
img_urls = [
    "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
    "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f",
]

queries = [
    "Is this person really big, or is this building just super small?",
    "Where was this photo taken?"
]

# Load images
images = []
for url in img_urls[:batch_size]:
    image = Image.open(requests.get(url, stream=True).raw)
    images.append(image)

# Prepare prompts
prompts = prepare_prompts(queries[:batch_size])

# Prepare batch inputs
inputs = processor(
    text=prompts, 
    images=images, 
    return_tensors="pt",
    padding=True
).to(model.device)

# Generate response ids for batch
gen_tokens = model.generate(**inputs, max_new_tokens=512, do_sample=False)

# Decode responses
print(f"Batch processing {len(images)} images:")
print("-" * 50)

for i in range(len(images)):
    input_length = inputs.input_ids[i].shape[0]
    response = processor.tokenizer.decode(
        gen_tokens[i][input_length:], 
        skip_special_tokens=True
    )
    print(f"Response: {response}")
    print("-" * 50)

Pipeline Usage

Click to expand/collapse code

from transformers import pipeline
from PIL import Image
import requests


pipe = pipeline(
    model="utter-project/TowerVision-9B", 
    task="image-text-to-text", 
    device_map="auto",
    dtype="bfloat16"
)

def prepare_prompt(query):
    conversation = [
        {
            "role": "user", 
            "content": f"<image>\n{query}"
        }
    ]
    
    # Format message with the towervision chat template
    return pipe.processor.apply_chat_template(
        conversation, 
        tokenize=False,
        add_generation_prompt=True
    )
    
    
img_url = "https://cms.mistral.ai/assets/a10b924e-56b3-4359-bf6c-571107811c8f"
image = Image.open(requests.get(img_url, stream=True).raw)
text = prepare_prompt("Is this person really big, or is this building just super small?")

outputs = pipe(text=text, images=image, max_new_tokens=300, return_full_text=False)
print(outputs)

Model Details

Input: Model accepts input text and images.

Output: Model generates text in multiple languages.

Model Architecture: TowerVision uses a multilingual language model based on Tower-Plus (2B and 9B parameters), paired with SigLIP2-patch14-384 vision encoder through a multimodal adapter for vision-language understanding.

Recommended Precision: We recommend using bfloat16 precision for optimal performance and memory efficiency when running TowerVision models.

Languages Covered: The model has been trained on 20 languages and dialects:

• European languages: English, German, Dutch, Spanish, French, Portuguese, Italian, Polish, Czech, Romanian, Norwegian (Bokmål & Nynorsk)
• Asian languages: Chinese (Simplified & Traditional), Japanese, Korean, Hindi
• Other languages: Russian, Ukrainian

Key Strengths:

• 🏆 Exceptional performance on culturally-aware benchmarks with deep understanding of cultural contexts and visual nuances
• 🌐 State-of-the-art results on multimodal multilingual translation benchmarks, enabling seamless cross-lingual visual communication
• 📊 Strong cross-lingual transfer capabilities across diverse vision-language tasks

Training Data

TowerVision models are trained on VisionBlocks, a comprehensive multilingual vision-language dataset comprising 6.31M samples across diverse categories:

Dataset	Samples	HF Link
VisionBlocks	6.31M	🤗 utter-project/VisionBlocks	Coming Soon

Dataset Statistics

• Total samples: 6.31M
• Created by our team: 1.21M samples (~19%)
• Human-collected/external: 5.10M samples (~81%)

Dataset Composition Overview

VisionBlocks contains samples across multiple categories with both English-only (63.1%) and multilingual (36.9%) data:

• Chart/Plot Reasoning: DVQA, ChartQA, PlotQA, TabMWP (~405K samples)
• General VQA: VQAv2, RLAIF-4V (~488K samples)
• Document VQA: DocVQA, TextVQA, ST-VQA, PixMo-Docs (~46K samples)
• Reasoning/Knowledge: A-OKVQA, OKVQA, AI2D, ScienceQA (~29K samples)
• Multilingual/Cultural: Pangea-Cultural, Pangea-Multi, PixMo-Cap-Translated, CulturalGround datasets (~1.6M samples)
• Specialized VQA: IconQA, InfographicVQA, Stratos (~34K samples)
• Counting/Math: TallyQA, PixMo-Count (~107K samples)
• Vision/Text: VBlocks-PixMo collections, EuroBlocks-SFT (~2.2M samples)
• Video/Text: LLaVA-Video collections (~1.4M samples)

Collection Types: Human-annotated, synthetically generated, and professionally translated data ensuring high quality and cultural diversity across 20+ languages.

Evaluation

All evaluations were conducted using lmms_eval.

Multiple Purpose Multimodal Benchmarks

TowerVision demonstrates strong performance across diverse multimodal evaluation benchmarks:

Multimodal Multilingual Translation Tasks

TowerVision excels particularly in multimodal multilingual translation benchmarks, demonstrating state-of-the-art cross-lingual visual communication capabilities:

Supported Languages Performance

✅ Fully Supported: English, German, Dutch, Spanish, French, Portuguese, Italian, Polish, Czech, Romanian, Norwegian, Chinese, Japanese, Korean, Hindi, Russian, Ukrainian

📊 Benchmark Coverage: Our models are evaluated across diverse multilingual vision-language tasks, demonstrating strong cross-lingual transfer capabilities and exceptional performance in culturally-aware benchmarks.

Citation

If you find TowerVision useful in your research, please consider citing the following paper:

@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}
}

Model Card Contact

For errors or additional questions about details in this model card, contact the research team.

Acknowledgments

TowerVision builds upon the excellent work of:

• LLaVA-NeXT for the foundational vision-language architecture
• Tower-Plus language models for multilingual capabilities
• SigLIP2 for robust vision encoding
• The broader multilingual NLP and multimodal communities