laya-mlx plays Snake at 60 decisions per second on an M3 Max
介绍比Jev快50倍，在你设备上跑的laya-mlx! 只在你的设备上占用最高1G内存 Laya是一个开源的类似于Jev的,基于文本输出概率的分类系统 我将其移植到MLX,并且做了一些性能优化! 视频中就是这个模型在我的本地M3Max上玩贪吃蛇 这个模型能够以每秒决策60次的速度玩贪吃蛇！
Project: laya-mlx→The Laya project directory
Everything the world is building on Laya.
Laya turns any text into typed decisions in milliseconds, on your own machine. Here is what it does, and 952 projects people built with it.
real run · laya-mlx
Input text
“Hi, we were billed twice for March. Please refund the duplicate today or we will cancel our plan.”
laya-mlx · 146 input tokens · 0 output tokens
Which team should handle this ticket?
choice✓ billing98%
technical1%
sales1%
How urgent is this?
scorenot urgent4%
soon15%
✓ blocking81%
Does the customer threaten to cancel or leave?
yes / no✓ yes84%
no16%
Decided in 30.7 ms · 3 typed answers · one forward pass
Real outputs from Laya, not a mock-up. Run it yourself →
- Projects
- 952
- New this week
- 952
- Platforms
- 18
Seen on X this month
All demos →
Local Laya beats Jev at Tetris on a MacBook Air
Local Laya moggs Jev at @grok 4.7-built Tetris 🧩 An open-weights System One model called Laya, beat cloud-based Jev at playing Tetris by making decisions 11 times faster, running locally on a 16GB MacBook Air! Run AI models locally ->
Jev decision layer routes tasks and recovers from tool failures
jev + opus 5.5... i simply can't comprehend why everyone isn't building this yet. in my workflow, this cut costs and time by ~80%. i think it's one of the best ways to use it. → pick relevant project notes before loading the context → route suitable tasks to a faster worker → choose a recovery path when a tool fails → run focused checks before the full test suite opus handles the hard reasoning. jev picks from options the harness prepares and validates. i explain how to build the decision layer in the article below:
Laya plays Tetris locally with decisions in about 27 milliseconds
This is NOT Jev. Open source. Runs on your laptop. Decides in ~27 ms, about 200× faster than waiting on a hosted LLM. Here it is playing Tetris by itself 👇
laya-coreml runs 99.5% of operations on ANE at 3.7 ms per decision
Thanks for the model, we were able to port Laya to coreml with 99.5% of the ops on ANE + benchmarked too. it is now blazing fast with 3.7 ms per decision on an M5 Pro. Release: Models:
Project: FluidUse→A step-by-step Jev build guide with code and architecture
everything you need to start building with jev, in one article. code, architecture, diagrams... everything you need to follow the build and make it your own.
Directory
+ Submit yoursM
laya-multilingual-GGUF
mys
Compiles the multilingual Laya decision model into GGUF files using ggmlc
6.1k·HF model
A
laya-multilingual-coreml-ane
aac6fef
Packages the multilingual Laya decision model as a Core ML bundle for Apple devices
471·HF model
laya-jev-lab
yibie
Compares Jev and Laya decision models and evaluates a local-first inference cascade
9·GitHub repo
laya-rust
aovestdipaperino
Implements pure-Rust inference for Laya using the Candle framework
9·GitHub repo
laya-candle
Trystan-SA
Runs Laya checkpoints locally from Rust using the Candle inference framework
9·GitHub repo
laya-rs
redwolf2019
Provides a Rust HTTP inference runtime and server for multilingual Laya models
9·GitHub repo
zlaya
dip-proto
Runs Laya inference locally in Zig, including native and WebAssembly targets
9·GitHub repo
laya-rs
apiplant
Reimplements Laya inference and training in Rust using the Candle framework
8·GitHub repo
laya-apple
tc3oliver
Runs Laya locally on Apple Silicon with MLX GPU and Apple Neural Engine support
8·GitHub repo
gg-friggin-ez
ItisShikhar
Screens Node.js text for profanity and toxicity using Jev and Laya System 1 models
7·GitHub repo
fast-jev-compaction-laya
kaiyes
Compacts OpenCode context using decisions from a locally served Laya model
7·GitHub repo
Laya4j
githubMJ
A Java 17+ SDK runs Laya typed-decision models through ONNX Runtime
7·GitHub repo
kevala
bvolpato
A Rust WebAssembly engine runs Laya and other decision models locally in browsers
7·GitHub repo
A
laya-coreml
aac6fef
Packages the Laya decision model as a Core ML bundle for Apple devices
128·HF model
laya
he-jev
A Hugging Face repository provides Laya multilingual and typed-decision model checkpoints
6·GitHub repo
laya_router
glukicov
A Python model router compares local Laya decisions with a GPT-5 nano routing model
6·GitHub repo
dsh-laya
PerryLink
A Cordis plugin exposes Laya typed decisions as tools for DeepSeek Harness
6·GitHub repo
jevbench
dhruvmehra
A reproducible benchmark compares JEV, Laya, and other classifiers across datasets and metrics
6·GitHub repo
layaForWorkflows
vishalmysore
Runs browser-based Laya decisions to automate branching workflows
6·GitHub repo
laya-ultrafast
xuancuongdoo
A Python browser agent uses local Laya decisions to select actions from indexed page elements
6·GitHub repo
W
laya-multilingual-GGUF
Weidows
Converts the multilingual Laya model to GGUF with quantized variants
2.5k·HF model
F
laya-gguf
fr0stbit3
Converts Laya to GGUF and provides quantized weights with a separate decision head
1.6k·HF model
S
laya-mind2web-browser-agent
ShaunSpark
Fine-tunes Laya on Mind2Web for browser automation decisions
110·HF model
A
laya-typed-decisions-mlx
aac6fef
Runs Laya typed-decision inference natively on Apple silicon with MLX
5·HF model