---
license: apache-2.0
library_name: aether
tags:
  - diffusion
  - masked-diffusion
  - language-model
  - mdlm
datasets:
  - wikitext
pipeline_tag: text-generation
---

# ameyg910/aether-55m

A masked (absorbing-state) diffusion language model trained with
[Aether](https://github.com/ameyg910/aether).

Unlike an autoregressive model, generation does not proceed left to right. The
model starts from an all-`[MASK]` sequence and unmasks progressively, so the
number of forward passes (**NFE**) is a knob rather than a function of sequence
length.

## Model details

| | |
| --- | --- |
| Architecture | bidirectional DiT denoiser, AdaLN-Zero time conditioning |
| Objective | MDLM / SUBS masked-diffusion loss |
| Parameters | 55,543,634 |
| Width / depth / heads | 384 / 6 / 6 |
| Context length | 1024 |
| Vocabulary | 50,258 (GPT-2 + `[MASK]`) |
| Training steps | 30,000 |
| Tokens seen | 3,932,160,000 |
| License | Apache-2.0 |

## Evaluation

Measured with `aether-eval`; see
[the evaluation protocol](https://github.com/ameyg910/aether/blob/main/docs/evaluation.md).

| metric | value |
| --- | --- |
| NELBO (nats/token) | 7.136 |
| Bits per dim | 10.3 |
| Perplexity (upper bound) | 1257 |
| MAUVE | 0.999 |
| distinct-2 | 0.971 |
| Sampler / steps | ancestral / 128 |

> **Perplexity here is an upper bound, not an exact likelihood.** A masked
> diffusion model has no exact factorization of `log p(x)`; what is reported is a
> Monte Carlo estimate of a variational bound. It is comparable to other diffusion
> models evaluated the same way, and **not** directly comparable to an
> autoregressive model's exact perplexity, which would flatter the AR model.

## Usage

Serve it:

```bash
pip install "aether-dlm[serve]"
aether-serve serve.model_version=hf:ameyg910/aether-55m@v1.0.0

curl -X POST localhost:8000/generate \
  -H 'content-type: application/json' \
  -d '{"n_samples":2,"length":64,"steps":64,"sampler":"ancestral"}'
```

Or load it directly:

```python
import torch
from huggingface_hub import hf_hub_download
from aether.models.loading import build_model_from_checkpoint
from aether.diffusion.samplers import sample

path = hf_hub_download("ameyg910/aether-55m", "latest.pt", revision="v1.0.0")
model, config = build_model_from_checkpoint(
    torch.load(path, map_location="cpu", weights_only=False)
)
out = sample(model.eval(), batch=2, length=64,
             mask_token_id=config.vocab_size - 1, steps=64)
print(out.tokens.shape, "NFE:", out.nfe)
```

## Intended use

Research and demonstration of masked diffusion language modelling: studying the
NFE-quality tradeoff, comparing sampling strategies, and as a fixture for
inference-serving work.

## Limitations

- **Small and undertrained.** 55,543,634 parameters and roughly
  3,932,160,000 tokens. It captures vocabulary and local phrasing, not
  long-range coherence or factual grounding.
- **Unconditional.** There is no prompt input; it generates from an all-`[MASK]`
  sequence. Prompt-conditioned infilling is a natural extension the architecture
  supports but this release does not implement.
- **No alignment of any kind.** No instruction tuning, no safety filtering, no
  RLHF. Output may be offensive, false, or nonsensical.
- **Inherits its corpus.** Trained on wikitext, and reproduces the biases and
  errors in it.

Not suitable for production text generation, question answering, or any use where
output correctness matters.

## Citation

```bibtex
@software{aether,
  author = {Gupta, Amey},
  title  = {Aether: a production platform for masked diffusion language models},
  year   = {2026},
  url    = {https://github.com/ameyg910/aether}
}
```