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zerodegress/laya-gguf
laya-gguf is a machine learning model from zerodegress. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for laya. The card lists the license as apache-2.0.
GGUF version of convaiinnovations/laya, packaged for the laya-rust inference engine.
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From the Hugging Face model README
GGUF version of convaiinnovations/laya, packaged for the laya-rust inference engine.
Laya is a non-autoregressive System One decision model: 421M parameters, a ModernBERT-large encoder plus a decision head. Give it a state (text, email, ticket, JSON) and typed questions; it returns typed answers with probabilities in a single forward pass. It never generates text.
| file | weight type | bytes | sha256 |
|---|---|---|---|
laya-f16.gguf | F16 | 848,158,048 | 62db2affc0fe4b9f2538ba61299e5fd8bf876f50f765089a343cd4113eef21a4 |
laya-f32.gguf | F32 | 1,690,745,696 | 63429326ecfe75faff55b2a637b67a3012603057532cc7ebffdbc308798abf16 |
Both hold the same values — the published checkpoint is F16, and the F32 file is a lossless widening of it — and they return identical answers. Each file is self-contained (weights, architecture, tokenizer, fitted calibration temperatures), so no sidecar files are needed.
git clone https://github.com/zerodegress/laya-rust && cd laya-rust
cargo build --release --no-default-features --features mlx # Apple Silicon; cuda is the default feature
./target/release/laya test -m /path/to/laya-f16.gguf - < request.json
This is not a llama.cpp GGUF: the architecture is laya, which llama.cpp does not know.
Weights: Convai Innovations and Laya contributors, Apache-2.0 — see convaiinnovations/laya. Converted with the engine's own laya convert, and verified tensor by tensor against the published checkpoint.