Laya: The Open-Source Alternative to Jev
Laya is an open decision model that answers the same three question types as Jev — choice, score and noul — without sending anything to an API. It is a non-autoregressive model of roughly 421 million parameters, which is why it fits in 1–2 GB of RAM, runs on a laptop CPU, and has been ported to Apple's MLX and Core ML stacks.
It became the centre of the launch-week argument: the maintainers say the architecture was built and open-sourced about a year before Jev shipped, and the r/LocalLLaMA thread asking who was first is the single most-upvoted discussion of the whole release. For builders the argument matters less than the practical consequences: no API key, no per-call bill, no data leaving the machine, and no terms that restrict benchmarking.
What it does
- Evaluates typed decisions — one option out of a set, a number inside a range, or a calibrated truth value — in a single forward pass.
- Returns probabilities, not just labels, so you can threshold and queue low-confidence answers.
- Covers 100+ languages from one checkpoint family, with separate English and multilingual weights.
- Ships alongside a public decision benchmark, which is how it can claim a score at all against a model whose terms restrict public testing.
Numbers worth knowing
| Metric | Laya | Reference |
|---|---|---|
| Parameters | ~421M | English checkpoint; multilingual variant is smaller |
| Memory | ~1–2 GB RAM | Comfortable on CPU-only boxes |
| Latency, short decisions | ~35 ms on GPU | Reported on the model card |
| Latency, MLX on M3 Max | 7–14 ms | Validator port with published benchmarks |
| Latency, Core ML + Neural Engine | ~5 ms | Validated port, reproducible speed and energy runs |
| Routing accuracy | 0.766 | vs 0.727 for the hosted model, on the public decision benchmark |
Treat the accuracy row as a starting point, not a verdict: both sides were moving weekly during launch, and the eval set is shared and public. The honest summary is that a 400M-parameter model running locally lands in the same range as the hosted frontier model on this task family.
How to get it
- Python:
pip install layafor the reference package. - JavaScript / Node:
@receptron/layaruns the ONNX export throughonnxruntime-node. - Apple silicon: Core ML port for the Neural Engine, MLX port for GPU.
- Weights: published on Hugging Face by the ConvAI Innovations team, with a browser playground for quick sanity checks.
When to use Laya instead of Jev
- You need decisions on data that cannot leave the device or the VPC.
- You are shipping to edge hardware — phones, laptops, on-prem boxes — where a network round trip is the bottleneck.
- You want to fine-tune, quantise, or benchmark freely.
- You are prototyping and do not want a metered bill while you iterate on question wording.
And when to stay hosted: you want the strongest calibration on hard, messy input, you do not want to operate inference, or you need the vendor's own support and terms behind a production decision.
Last updated: 2026-09-21 · sources & corrections