Laya on ONNX Runtime
ONNX is how most non-Python Laya runtimes work. An exported graph plus the tokenizer and calibration config is enough to answer typed questions with ONNX Runtime, and several projects publish ready-made exports so you can skip the PyTorch conversion step.
76 projects
laya-review
x007007007
Provides local-first code review using Laya decision rules through ONNX Runtime
laya-snapdragon
piffie
Runs Laya typed decisions on Snapdragon X NPUs through ONNX Runtime and Qualcomm QNN
laya-models
dockndevai
Provides quantized ONNX builds of Laya for browser inference
laya-hexagon-npu
EricYu123456
Deploys Laya inference on Qualcomm Hexagon NPU using ONNX Runtime
laya-vs-llms
Ujjwal3115
Benchmarks Laya against hosted language models on developer commit triage and CI safety decisions
vybir
Galactic717
Runs Laya locally with conformal error guarantees and includes score-based game-learning demos
laya
EduardoGHdez
A Ruby library runs the Laya decision model locally with ONNX Runtime
laya-s-dungeon
ValchanOficial
A tactical auto-battler that uses Laya through ONNX Runtime to choose actions each turn
layar
nullean
A .NET port of Laya provides ONNX and TorchSharp inference backends and CLI tools
laya-go
metalagman
Provides an embeddable Go runtime for local Laya inference and typed decision routing
laya_demo
Cokefish9527
Deploys Laya ONNX weights locally on CPU and serves decisions through an HTTP API
laya-node
roryyu
Provides a Node.js SDK for local Laya typed decisions using Transformers.js and ONNX
laya-cpp
samiul000
Implements native C++ Laya inference with ONNX Runtime, benchmarking, and INT8 quantization
vibe-check
ghobs91
Detects ragebait and spam in real time using Laya ONNX
laya-multilingual-qnn
asopitech
Runs Laya's multilingual decision model on Qualcomm Hexagon NPU through ONNX Runtime QNN
jev-systemone-local
katya4oyu
Plans a local Jev-compatible System One server with MLX, Core ML, and ONNX Laya backends
ruby_llm-providers-laya
codenamev
A RubyLLM provider uses local ONNX Laya checkpoints to answer judge questions
ruby_decision_model-providers-laya
codenamev
Adds a local ONNX-backed Laya provider to the Ruby decision-model gem
usejev
ali-master
Serves Laya through native ONNX inference on Bun with a TypeSafe-compatible API and bilingual playground
laya-universal
abusuraihsakhri
Provides cross-platform inference utilities for Laya checkpoints using ONNX Runtime and MLX
laya-cuda-bench
bhushankinge
Benchmarks Laya inference throughput, latency and cost across NVIDIA GPUs and serving backends
xcanv-models
attachemd
Provides opt-in ONNX Laya decision-model packs quantized for local inference
jev-course-demo
clchrf
Evaluates university AI course plans in the browser using quantized Laya ONNX inference
laya-ui
bharath-ui1027
Provides a multilingual Laya decision engine with UI components and optional serving integrations
Use a pre-converted export
inferenceprince/laya-onnx is an fp16 export of the English checkpoint. From its model card:
pip install onnxruntime huggingface_hub tokenizers numpy
hf download inferenceprince/laya-onnx --local-dir laya-onnx
The card includes a full Python example that builds the prompt, runs the graph and applies the fitted temperature. It reports load-to-first-answer of 3 to 5 s against 25 to 35 s for PyTorch on an Intel i5-14400F. The official package also has a laya[onnx] extra with an ONNXAgent class in laya.onnx_agent.
INT8 on CPU
edgejev converts Laya to ONNX, quantizes it to INT8 and serves it:
uv tool install "edgejev[build]"
edgejev build --backend laya --out ./jev-int8
Its README reports 15.6 ms per question for the INT8 multilingual model on a 4 vCPU Xeon (Cascade Lake) and 32.1 ms in fp32, with the model shrinking from 1290 MB to 324 MB. The same table shows AG News accuracy dropping from 92.8% to 91.2%.
ONNX in other languages
- Node.js: @receptron/laya, using receptron/laya-onnx.
- Java: Laya4j.
- Go: laya-onnx (MstyAI).
- Ruby: ruby-laya and laya (EduardoGHdez).
- Windows GPUs through DirectML: laya-windows.
- Local server: ollaya runs ONNX Runtime on CPU and CUDA.
- Browser: layaForWeb with ONNX Runtime Web.
Caveats
The graph returns raw logits. Divide by the fitted temperature from rl_agent_config.json before the softmax, or the probabilities mean nothing. Quantized builds drift from PyTorch: the layaForWeb README reports 97.9% top-answer agreement for its int8 build, and it notes that its confidence values are approximate because calibration was fitted on the full-precision model.
More ways to use Laya