Small models.Any CPU.Offline.
QLNI builds SHADOW: open language models trained in ternary from the first step, with a frozen binary vocabulary. A capable assistant in tens of megabytes, on the computer you already own.
Useful AI that people can own
Most AI lives in data centres and is rented by the token. SHADOW goes the other way: models small enough to download in seconds, fast on an ordinary laptop processor, and open so anyone can adapt them to their own work.
Every word SHADOW knows is a fixed 512-bit code instead of a trained table of floats. The vocabulary that fills most of a small model becomes a few megabytes of bits.
Tens of megabytes, not gigabytes.
Ternary weights, hand-written C kernel.
Fully offline; your files stay on your disk.
Weights, code and benchmarks released.
Two models.
Zero cloud.
SHADOW‑250M Instruct
Trained on 30 billion tokens. A frozen 512-bit vocabulary of 131,072 tokens and a body stored below 2 bits per weight; reads an archive on disk far beyond its window.
SHADOW‑50M Instruct
44 million ternary parameters with exact circuits inside the model. Its frozen table learned 8,600 new words without retraining.
The SHADOW-250M launch, posted by the founder.
139 GitHub stars · 360+ Hugging Face downloads · outside developers already benchmarking against SHADOW.
Is the embedding table overpriced?
In a small model the vocabulary is the largest single part: 63% of Gemma 3 270M. Four models with the same body and the same 2 billion tokens differ only in their vocabulary.
Then: a SHADOW that sees and hears as well as reads, released as one offline package.
Build it with us
Grants, compute partners, research collaborators and early users are all welcome. QLNI is founded by Sai Kiran Bathula in New South Wales, Australia: independent and self-funded.