---
library_name: transformers
tags:
- multimodal
- multilingual
- vlm
- translation
language:
- en
- de
- nl
- es
- fr
- pt
- uk
- hi
- zh
- ru
- cs
- ko
- ja
- it
- pl
- ro
- nb
- nn
base_model:
- utter-project/TowerVision-9B
pipeline_tag: video-text-to-text
license: cc-by-nc-sa-4.0
---

# Model Card for TowerVideo

<p align="left">
<img src="Tower.png" alt="TowerVision Logo" width="300">
</p>

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.

- **Point of Contact**: X (add some email here)
- **License**: Apache 2.0
- **Model Family**: TowerVision (2B, 9B variants)
- **Context length**: 8192 tokens
- **Languages**: 20+ languages including European, Asian, and other language families

<span style="font-size: 1.2em;"><strong>🌟 Try TowerVision</strong></span>: [Project Page](https://guilhermeviveiros.github.io/TowerVision.io/) | [Code Repository](https://github.com/GuilhermeViveiros/LLaVA-NeXT)

## Available Models

<p align="left">

| Model | Parameters | HF Link |
|-------|------------|---------|
| TowerVideo-2B | 2B | [🤗 utter-project/TowerVision-2B](https://huggingface.co/utter-project/TowerVideo-2B)
| TowerVideo-9B | 9B | [🤗 utter-project/TowerVision-9B](https://huggingface.co/utter-project/TowerVideo-9B)

## How to Use TowerVision

### Quick Start with Transformers

<details open>
<summary>Click to expand/collapse code</summary>

```python
mport torch
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration

# Load the model in half-precision
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
    "utter-project/TowerVideo-2B", 
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "utter-project/TowerVideo-2B"
)

# Use your local video
video_path = "your_video_path.mp4"

# Conversation using the same template
conversation = [
    {
        "role": "user",
        "content": [
            {"type": "video", "path": video_path},
            {"type": "text", "text": "\n<video>\nIWhat is the video about?"},
        ],
    },
]

# Apply the chat template
inputs = processor.apply_chat_template(
    conversation,
    num_frames=8,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    add_special_tokens=True,  # ensures <video> token is inserted
    return_tensors="pt"
).to(model.device, torch.float16)

# Generate response
out = model.generate(**inputs, max_new_tokens=60)

# Decode output
decoded = processor.batch_decode(
    out, 
    skip_special_tokens=True, 
    clean_up_tokenization_spaces=True
)

print(decoded)
```

</details>

## Model Details

**Input**: Model accepts input text, images and video.

**Output**: Model generates text in multiple languages.

**Model Architecture**: TowerVideo uses a multilingual image-language model based on [Tower-Plus](https://huggingface.co/utter-project/TowerVision-2B) (2B and 9B parameters), paired with [SigLIP2-patch14-384](https://huggingface.co/google/siglip2-so400m-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
- **📊 Strong cross-lingual transfer capabilities** across diverse vision-language tasks

## Training Data

TowerVision models are trained on a video/text subset of **VisionBlocks**, a comprehensive multilingual vision-language dataset comprising **6.31M samples** across diverse categories:

| Dataset | Samples | HF Link |  |
|---------|---------|---------|-------|
| VisionBlocks | 6.31M | [🤗 utter-project/VisionBlocks](https://huggingface.co/datasets/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](https://github.com/EvolvingLMMs-Lab/lmms-eval).

### Multiple Purpose Multimodal Benchmarks

TowerVision demonstrates strong performance across diverse multimodal evaluation benchmarks:

<img src="mc-eval1.png" alt="Multiple Purpose Multimodal Benchmarks Results" width="600">

### Multimodal Multilingual Translation Tasks

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

<img src="mc-eval2.png" alt="Multimodal Multilingual Translation Results" width="600">

### 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 TowerVideo 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}
}
```

## 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](https://github.com/GuilhermeViveiros/LLaVA-NeXT)** for the foundational vision-language architecture
- **[TowerVision-9B](https://huggingface.co/utter-project/TowerVision-9B)** vision-language model with multilingual capabilities
- **[SigLIP2](https://huggingface.co/google/siglip2-so400m-patch14-384)** for robust vision encoding
- The broader multilingual NLP and multimodal communities