👋 Meet void.1
State of the art, small language model pretrained from scratch on a diverse set of high-quality texts and an internal symbolic kernel. This is the first step into a series of models designed for fine-grained understanding of abstract/symbolic reasoning on text while being grounded on english.
Comparison
| Model | Params | HellaSwag | PIQA | ARC-E | ARC-C | ArithMark-3 | Intelligence Index |
|---|---|---|---|---|---|---|---|
| void.1* | 90.15M | 38.68% | 67.46% | 47.31% | 28.16% | 44.80% | 23.92 |
| 100M-exp | 98.16M | 37.78% | 66.97% | 49.83% | 27.22% | 40.00% | 22.47 |
| Rose-1.5-Medium | 98.28M | 38.09% | 64.80% | 47.22% | 27.13% | 40.70% | 21.07 |
| tinctura-v1 | 96.2M | 37.96% | 65.61% | 47.98% | 25.77% | 38.40% | 20.81 |
| Surjo-100m | 97.7M | 35.05% | 63.87% | 47.64% | 25.85% | 38.90% | 18.86 |
Details
We used the revision on step 900,000 for evaluations which trained for ~12 days on a single RTX PRO 6000 gpu. It trained on 131.69 billion bytes which is between 32.92B and 43.90B standard tokens, around ~38 billion bpe tokens on average. For more details on the evals and inference, please have a look at the official notebook. For precise training details, check the training's config file and metrics.
@misc{dotlabs,
title = {void: bytes is all you need},
author = {appvoid},
year = {2026},
url = {https://huggingface.co/dotlabs/void.1}
}
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