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license: apache-2.0
datasets:
- HuggingFaceFW/fineweb
- HuggingFaceFW/fineweb-2
language:
- en
- uk
- zh
- hi
tags:
- pixel
- mae
- vit
- tokenization-free
---
# MPIXEL
MPIXEL, introduced in [Scaling Multilingual Pretraining of Pixel Language Models](https://arxiv.org/abs/2610.29681), is a family of multilingual pixel language models, which operate on visually rendered text. Our main models, `mpixel-base` (86M parameters) and `mpixel-large` (307M parameters), are pretrained on sixteen languages that span ten writing systems. MPIXEL uses adaptive rendering: it renders text as grapheme bigrams by default, Chinese, Japanese and Korean text one character per patch, and Arabic and Hebrew text word by word.
This model is `mpixel-4s`. It follows the [pixel-m4](https://aclanthology.org/2025.emnlp-main.1504/) setup and is pretrained on the same four languages (English, Hindi, Chinese and Ukrainian), but its text rendering, rotary positional embeddings, data source and per-language data amount are matched to mpixel-base to remove confounding factors.
## Basic usage
To use our models, please first install our [`mpixel` package](https://github.com/ilkerkesen/mpixel). Here is an example of loading a pretrained model and encoding input text:
```python
from transformers import AutoModel
import mpixel.models.auto_register # registers MPIXEL with the Auto classes
from mpixel.processing import MPIXELProcessor
processor = MPIXELProcessor.from_pretrained("Team-PIXEL/mpixel-4s")
model = AutoModel.from_pretrained("Team-PIXEL/mpixel-4s")
inputs = processor(["Hello world!", "The quick brown fox jumps over the lazy dog."])
outputs = model(**inputs)
print(outputs.last_hidden_state.shape) # torch.Size([2, 513, 768])
```
For further information, please take a look at our repository: [https://github.com/ilkerkesen/mpixel](https://github.com/ilkerkesen/mpixel).
## Citation
If you find MPIXEL beneficial for your research, please cite it,
```
@misc{kesen2026scalingmultilingualpretrainingpixel,
title={Scaling Multilingual Pretraining of Pixel Language Models},
author={Ilker Kesen and Jenny Kunz and Kasra Malihi and Desmond Elliott},
year={2026},
eprint={2610.29681},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2610.29681},
}
```
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