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Compact Language Model Index
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Chance-normalized evaluation of compact language models.
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Universal Computing Research
Universal Computing Research is an independent research organization focused on compact language models, efficient architectures, tokenization, and training methods that improve capability under limited compute and parameter budgets.
We release models, code, and experiments for open and reproducible research.
Model families
Limen
Limen is our family of small general-purpose language models.
The first release, Limen0.2B, is a 222.5M-parameter base model trained from scratch on 50B tokens. It uses a BoundlessBPE tokenizer that can learn tokens spanning whitespace, along with grouped-query attention and an experimental XSA projection.
Atom
Atom is our family of extremely small experimental language models.
These models are used to study compact transformer architectures, custom tokenizers, arithmetic representations, and training curricula at parameter counts below four million.
Research
Parametrized Random Projection
Parametrized Random Projection replaces dense learned projections with fixed random feature mixing and small learned input and output parameters. The goal is to reduce trainable parameter growth from quadratic to linear while retaining useful projection capacity.
Open source
Our models, code, and research artifacts are released for reproducible and practical research.