---
datasets:
- MultimodalUniverse/legacysurvey
- MultimodalUniverse/hsc
- MultimodalUniverse/gaia
- MultimodalUniverse/sdss
- MultimodalUniverse/desi
license: mit
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
pipeline_tag: any-to-any
library_name: aion
---

# AION-1: Astronomical Omnimodal Network

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![GitHub Repo](https://img.shields.io/badge/GitHub-Repo-blue?logo=github)](https://github.com/PolymathicAI/AION)
[![Paper](https://img.shields.io/badge/Paper-2510.17960-b31b1b.svg)](https://huggingface.co/papers/2510.17960)
[![arXiv](https://img.shields.io/badge/arXiv-2510.17960-b31b1b.svg)](https://arxiv.org/abs/2510.17960)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PolymathicAI/AION/blob/main/notebooks/Tutorial.ipynb)

**AION-base** is a 300M parameter large omnimodal model specifically designed for astronomical surveys, presented in the paper [AION-1: Omnimodal Foundation Model for Astronomical Sciences](https://huggingface.co/papers/2510.17960). It integrates 39 distinct astronomical data types and enables adaptation to a wide range of astronomical tasks through multimodal masked modeling.

Project Homepage: https://polymathic-ai.org/

## Model Details

-   **Architecture**: Encoder-Decoder Transformer (12 blocks each, 768 dim, 12 heads)
-   **Parameters**: 300M
-   **Training**: Multimodal Masked Modeling (4M) on astronomical survey data
-   **Modalities**: 39 data types including imaging, spectra, catalogs, and photometry

## Installation

Assuming you have PyTorch installed, you can install AION trivially with:
```bash
pip install polymathic-aion
```
For advanced installation options, including specific PyTorch versions or developer installations, refer to the [GitHub repository](https://github.com/PolymathicAI/AION).

## Usage

After installation, you can load the pretrained model and start analyzing astronomical data.

```python
import torch
from aion import AION
from aion.codecs import CodecManager
from aion.modalities import LegacySurveyImage, Z

# Load model and codec manager
model = AION.from_pretrained('polymathic-ai/aion-base').to('cuda')  # or 'aion-large', 'aion-xlarge'
codec_manager = CodecManager(device='cuda')

# Example: Prepare your astronomical data (e.g., a dummy Legacy Survey image)
# In a real scenario, 'your_image_tensor' would come from your dataset.
your_image_tensor = torch.randn(1, 4, 96, 96) # Example: batch_size=1, 4 bands, 96x96 resolution
image = LegacySurveyImage(
    flux=your_image_tensor,
    bands=['DES-G', 'DES-R', 'DES-I', 'DES-Z']
)

# Encode data to tokens
tokens = codec_manager.encode(image)

# Option 1: Extract embeddings for downstream tasks
embeddings = model.encode(tokens, num_encoder_tokens=600)
print(f"Extracted embeddings shape: {embeddings.shape}")

# Option 2: Generate predictions (e.g., redshift)
# For this example, we predict redshift (Z) from the image.
# The target_mask tells the model which modality to generate.
preds = model(
    codec_manager.encode(image),
    target_modality=Z,
)
print(f"Predicted redshift logits shape: {preds['tok_z'].shape}")
```

### Supported Data Types
AION-Base processes data from major astronomical surveys. Here's an overview of the supported categories:

| **Category**            | **Description**                         | **Token Name(s)**        |
|:------------------------|:----------------------------------------|:-------------------------|
| **Imaging (2)**         | Legacy Survey, HSC Wide                 | `tok_image_ls`, `tok_image_hsc` |
| **Catalog (1)**         | Legacy Survey catalog entries           | `catalog`                |
| **Spectra (2)**         | SDSS, DESI                              | `tok_spectrum_sdss`, `tok_spectrum_desi` |
| **Gaia (4)**            | BP/RP spectra, parallax, sky coords     | `tok_xp_bp`, `tok_xp_rp`, `tok_parallax`, `tok_ra`, `tok_dec` |
| **Gaia Photometry (3)** | G/BP/RP flux                            | `tok_flux_g_gaia`, `tok_flux_bp_gaia`, `tok_flux_rp_gaia` |
| **Legacy Survey (9)**   | g,r,i,z bands & WISE W1–W4 flux, E(B–V) | `tok_flux_g`,…,`tok_flux_w4`, `tok_ebv` |
| **Legacy Shape (3)**    | Ellipticity components & effective radius | `tok_shape_e1`, `tok_shape_e2`, `tok_shape_r` |
| **HSC Photometry (5)**  | g,r,i,z,y magnitudes                    | `tok_mag_g`,…,`tok_mag_y` |
| **HSC Extinction (5)**  | g,r,i,z,y extinctions                   | `tok_a_g`,…,`tok_a_y`    |
| **HSC Shape (3)**       | Shape components 11,22,12               | `tok_shape11`, `tok_shape22`, `tok_shape12` |
| **Other (1)**           | Spectroscopic redshift                  | `tok_z`                  |

More details and interactive examples are available in the [Colab Tutorial](https://colab.research.google.com/github/PolymathicAI/AION/blob/main/notebooks/Tutorial.ipynb).

## Resources

-   GitHub Repository: https://github.com/PolymathicAI/AION
-   Interactive Tutorial: https://colab.research.google.com/github/PolymathicAI/AION/blob/main/notebooks/Tutorial.ipynb

## License

This project is licensed under the MIT License. See the [LICENSE](https://github.com/PolymathicAI/AION/blob/main/LICENSE) file in the GitHub repository for full details.

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