| --- |
| license: apache-2.0 |
| base_model: convaiinnovations/laya |
| base_model_relation: quantized |
| tags: |
| - gguf |
| - laya |
| - system-one |
| - decision-model |
| - modernbert |
| --- |
| |
| # Laya — GGUF |
|
|
| GGUF version of [`convaiinnovations/laya`](https://huggingface.co/convaiinnovations/laya), packaged for the [laya-rust](https://github.com/zerodegress/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. |
|
|
| ## Files |
|
|
| | 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. |
|
|
| ## Run it with laya-rust |
|
|
| ```bash |
| 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. |
|
|
| ## Attribution |
|
|
| Weights: Convai Innovations and Laya contributors, Apache-2.0 — see [`convaiinnovations/laya`](https://huggingface.co/convaiinnovations/laya). Converted with the engine's own `laya convert`, and verified tensor by tensor against the published checkpoint. |
|
|
