Metadata-Version: 2.4
Name: aether-nn
Version: 0.1.0
Summary: Aether: A Post-Transformer Neural Architecture — 20x smaller, trains on phone CPU
Home-page: https://github.com/aether-nn/aether
Author: Aether Research
License: MIT License
        
        Copyright (c) 2026 Aether Research
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: Homepage, https://github.com/aether-nn/aether
Project-URL: Repository, https://github.com/aether-nn/aether
Keywords: deep-learning,transformer,neural-network,nlp,efficient-ai
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.0.0
Requires-Dist: numpy>=1.21.0
Dynamic: author
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python

# ⚡ Aether

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

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/)
[![PyTorch](https://img.shields.io/badge/PyTorch-2.0+-red.svg)](https://pytorch.org/)

---

## Install

**From GitHub (works right now):**
```bash
pip install git+https://github.com/YOUR_USERNAME/aether.git
```

**From PyPI (after publishing):**
```bash
pip install aether-nn
```

**Clone locally:**
```bash
git clone https://github.com/YOUR_USERNAME/aether.git
cd aether
pip install -e .
```

---

## 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`:**
```flux
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:**
```bash
python flux/flux.py run mymodel.fx
```

**Or use Flux CLI:**
```bash
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
```

---

## Python API

```python
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)
```

---

## Why Aether?

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

| | GPT-4 | Mamba | RWKV | **Aether** |
|---|:---:|:---:|:---:|:---:|
| Infinite context | ❌ | ⚠️ | ⚠️ | ✅ |
| Trains on phone | ❌ | ❌ | ❌ | ✅ |
| Model size (10B) | ~20GB | ~8GB | ~8GB | **~800MB** |
| Learns after deploy | ❌ | ❌ | ❌ | ✅ |
| True reasoning | ❌ | ❌ | ❌ | ✅ |
| Own language | ❌ | ❌ | ❌ | ✅ **Flux** |
| Speed on CPU | 1x | 4x | 4x | **20x** |

---

## Model Sizes

| Variant | Params | Ternary Size | Quality |
|---------|--------|-------------|---------|
| Aether-Nano | 3M | **0.6 MB** | Basic |
| Aether-Micro | 14M | **2.6 MB** | Good |
| Aether-Mini | 116M | **22 MB** | GPT-2 level |
| Aether-Base | 350M | **66 MB** | Strong |
| Aether-Large | 1.3B | **246 MB** | Excellent |

---

## 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
```

---

## Training

```bash
# 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
```

---

## License

MIT — free for everyone, including commercial use.

---

## Citation

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