Run Laya on your Mac
Apple Silicon is the best-covered platform in the Laya ecosystem. Independent ports run the upstream checkpoints on the MLX GPU, through Core ML on the Neural Engine, or inside native Swift apps. Each port publishes its own speed and fidelity measurements, all taken on the author's own Mac.
308 projects
jev-vs-laya
Cognition-Forge
Compares Jev and Laya variants across typed-decision tasks
laya-universal
abusuraihsakhri
Provides cross-platform inference utilities for Laya checkpoints using ONNX Runtime and MLX
laya-101
alejandro-torrero
A minimal starter that runs Laya on a Mac with laya-mlx
laya-vs-jev
alilibx
Benchmarks open-weight Laya models against TypeSafe Jev on labelled datasets
laya-mlx-voxel
yuxino
Builds voxel reliefs by using Laya-MLX to choose extrusion depths from image pixel statistics
dino-jump
ozbillwang
A browser runner game uses Laya scores to evaluate jump, duck, and run actions
TetrisGame_Laya
RehmanaliMomin
Plays Tetris by using a fine-tuned Laya model to choose piece rotations and drop columns
laya-local-http-server
ratheesh-aot
Serves the Laya typed-decision model through a local HTTP API using MLX or PyTorch
laya-vs-jev-traffic
ameeetgaikwad
Compares local Laya and cloud Jev in a real-time traffic-control simulation
laya-coreml-vs-jev-benchmark
sallout
Compares Laya and Jev on zero-shot intent classification benchmarks
laya-fine-tune-bn-eco-voice
nafi-ullah
Fine-tunes multilingual Laya for typed decisions in a Bangla e-commerce voice agent
nlaut
benkya
Runs natural-language browser tests with deterministic, vision-model, and Laya-based judgments
cut
harshpreet931
Uses Laya to label and cut unnecessary lines from posts in a browser or local app
laya-steering-lab
Hantlowt
Tests methods for specializing frozen Laya models and benchmarks their accuracy and runtime
laya-first-look
cvranjith
Explores base Laya checkpoints locally on Apple M4 and reports informal behavior and latency measurements
reflex-engine
chenshuai9101
A Python toolkit collects, calibrates, and automates routine decisions using local Laya
@gabedsam01/decision-maker
gabedsam01
Provides typed decisions for AI agents through a Pi adapter and local Laya or hosted Jev
laya-trader
antonellof
Uses Laya MLX to paper-trade crypto and stocks with a dashboard and backtesting tools
laya-shim
bunkerlab-net
Serves Laya or Laya-MLX behind TypeSafe's System One API route
laya-mlx-demo
jerryshao2012
Demonstrates an Apple silicon MLX conversion of Laya for typed decision inference
lagos-monopoly
vunderkind
Runs two local Laya agents through a Lagos-themed Monopoly game and creates animated replays
laya-classifier-finetuning
sallout
Fine-tunes Laya for custom text classifiers and optionally exports models to Core ML
morning-agent
shauryajain07
A macOS morning briefing CLI uses local Laya-MLX for typed decisions
custom-voice-activator
M47HIS
Provides local macOS voice dictation and desktop control with Laya decisions
Pick a runtime
MLX (Python). laya-mlx is the most widely used port. It needs Apple Silicon, Python 3.11+ and macOS 14+:
pip install laya-mlx
import laya_mlx as laya
agent = laya.load("aac6fef/laya-mlx")
result = agent.predict(
"I was billed twice. Please refund the duplicate.",
{"department": {"type": "choice",
"instructions": "Who should handle this?",
"criteria": ["billing", "technical", "sales"]}},
)
print(result["answers"]["department"])
Its README reports 13.42 ms P50 for one short question with the 421M English checkpoint and 7.39 ms with the 322M multilingual one, FP16 on an M3 Max.
Core ML and the Neural Engine. laya-coreml needs macOS 15+ and Python 3.11 to 3.13 (pip install laya-coreml). Its README reports 4.98 ms P50 for one short multilingual decision on the M3 Max Neural Engine. laya-apple runs the MLX GPU and the Neural Engine together and serves a Jev-compatible API:
pip install 'laya-apple[serve,ane]'
laya-apple serve # http://127.0.0.1:8642
Swift. laya-mlx-swift embeds Laya in a macOS app with MLX (about 804 MiB download). LayaKit is a Swift package over the laya-coreml bundles, and FluidUse ships a LayaManager for Core ML.
PyTorch MPS. The official laya package also runs on the Mac GPU; laya-mps wraps it in a local server with low-memory modes.
Caveats
- All of these are independent ports, not official Convai Innovations releases. Most publish fidelity checks against upstream answers; read them.
- The Neural Engine bundle in laya-coreml has a 96-token total limit covering question, options and state. Use the 1024-token general bundle for longer input.
- Latency numbers are single-machine measurements. Your Mac, input length and question count will change them.
More ways to use Laya