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
laya-model-lora-finetune-mps
kotari4u
Fine-tunes Laya with LoRA on Apple Silicon and exports model checkpoints and adapters
Laya_Jev_opensource_guide
gotree94
Explains how to self-host Laya and Jev, including setup, fine-tuning, and benchmarks
distill-public
abhi-sama
Fine-tunes local Laya decision models from synthetic data and evaluates and exports calibrated models
go-laya-mlx
eiri
Provides Go inference for the Laya model on Apple Silicon using MLX
juggernaut
prayingperceptions
Provides a single-goal agent template using laya-mlx for local typed decisions on Apple Silicon
laya-control
minhoyooDEV
Coordinates local datasets, training jobs, evaluation, and releases for Laya
laya-ctl
r1cc4rd0m4zz4
Manages laya-serve as a background daemon with memory profiles and readiness probes
open-laya
yeohjarvis-source
Serves Laya text and multimodal inference through an authenticated MLX API
mlx-quantization-lora-eval
D-Harshith
Benchmarks MLX quantization and LoRA alongside Laya encoder classifiers on GitHub issue classification
@fllstck/mlayax
kay-is
Runs the Laya typed-decision model in Node and Bun on Apple Silicon using MLX
laya-mlx
Thar-Paing123
A native MLX runtime for running Laya typed-decision models on Apple silicon
laya-coding-harness
MysterTech
Local advisor that uses laya-mlx to suggest model, effort or planning for Claude Code and Codex
laya-workiq-routing
hieunhums
A fine-tuned Laya model routes Microsoft 365 workplace questions to Work IQ sources
laya-demo
vighnesh-chavan
Demonstrates Laya typed decisions in Doom and a custom pygame shooting game
laya-computer-use
adityachauhan0
Provides a macOS MCP server that uses Laya MLX to choose computer-use actions
FaradayLayaBenchmark
kurtfi
Benchmarks local models for MCP and agent security with a fail-fast evaluation pipeline
laya-fine-tuning
bnap00
Guides fine-tuning Laya on a 6 GB GPU with Pac-Man and Pigeonhole examples
laya-kit
Berk-Unsal
Supports offline Kubernetes incident triage using the laya-mlx model
laya-pinyin-ime
stevelikesrhino
Provides a private macOS pinyin input method with an optional Laya-based candidate ranker
decision-model-arena
dante01yoon
Compares four decision models, including local Laya, across games and image-based decisions
d1-ane
nathanrchn
Converts and benchmarks decision models on Apple Neural Engine, with a Tetris demo
layaball-ios4
mannanshukla
Provides a Magic 8-Ball and decision games powered by the OpenRelay Laya decisions model
feedcast
TimurAkhtemov
Produces local narrated news episodes using RSS feeds and models including Laya
Jev-Laya-Demo
johanlrdahl
Demonstrates Laya ticket triage against local Ollama models with typed decisions and quality reports
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