aether-nn

⚡ Aether

A neural architecture that makes Transformers obsolete. With its own programming language: Flux.

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Install

From GitHub (works right now):

pip install git+https://github.com/YOUR_USERNAME/aether.git

From PyPI (after publishing):

pip install aether-nn

Clone locally:

git clone https://github.com/YOUR_USERNAME/aether.git
cd aether
pip install -e .

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Flux Language

Aether comes with Flux — its own programming language for neural networks.
Write a model in 10 lines instead of 500.

Create a file mymodel.fx:

model ChatBot {
    size: micro
    vocab: 32000
    context: infinite

    memory {
        working:  512
        episodic: 2048
        semantic: 8192
    }

    block * 6 {
        attend fractal(scales=3, heads=8)
        remember hierarchical()
        think sparse_moe(experts=4)
    }

    quantize: ternary
}

train ChatBot {
    data: "data.txt"
    steps: 10000
    batch: 4
    lr: 3e-4
    device: auto
    save: "./checkpoints"
}

generate ChatBot {
    prompt: "Hello"
    tokens: 200
    temperature: 0.8
}

Run it:

python flux/flux.py run mymodel.fx

Or use Flux CLI:

python flux/flux.py check mymodel.fx    # syntax check
python flux/flux.py compile mymodel.fx  # show generated Python
python flux/flux.py new MyModel         # create template

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Python API

from aether import AetherModel, AetherConfig

config = AetherConfig.nano()   # 0.6 MB
model = AetherModel(config)

import torch
ids = torch.randint(0, 256, (1, 32))
out = model(ids)
print(out["logits"].shape)  # (1, 32, 256)

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Why Aether?

The Transformer solved one problem: parallelizing RNNs.
Aether solves six problems of the Transformer simultaneously.

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Model Sizes

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Architecture

5 innovations in one unified system:

[1] Fractal Sparse Attention   O(n·log n) vs O(n²)
[2] Hierarchical Memory        Infinite context
[3] Asymmetric Depth Routing   10-50x compute savings
[4] Causal World Model         True reasoning
[5] Continuous Learning        Adapts at inference time

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Training

# With Flux (recommended)
python flux/flux.py run flux/examples/nano.fx

# With Python directly
python train/train.py --config nano --data your_text.txt

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License

MIT — free for everyone, including commercial use.

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Citation

@misc{aether2026,
  title={Aether: A Post-Transformer Architecture},
  year={2026},
  url={https://github.com/YOUR_USERNAME/aether}
}