laya-mlx
by mizorewww
Runs Laya typed-decision models locally on Apple Silicon using MLX
Native MLX runtime for Laya typed decision models — 7–14 ms short decisions on M3 Max. No text generation, PyTorch, or cloud API.
On X
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→
Laya-mlx processes 20 Chinese support tickets in 25 ms
Jev 发布才 8 天，开源替身已经一堆了，今天诺基亚也来蹭热闹了😅 我在 MacBook 上，让官方 Jev 和 3 个开源方案同跑 20 张中文工单🫱 Laya 25ms，比 Jev 快 50 倍 djev 509ms 官方 Jev 1.3 秒 AnyJev 将近 4 秒 仓库都在这： Laya（Mac 版） djev（Mac 版） AnyJev 但快的，不一定对 👇
Project: laya-mlx→
laya-mlx plays Snake at 60 decisions per second on an M3 Max
Laya-MLX is an open source classification system similar to Jev but 50x faster, running locally on device with a maximum of 1GB memory and making decisions at 60 per second, fast enough to play Snake on an M3 Max at full speed. Repo here:
Project: laya-mlx→
laya-mlx plays Snake locally at 75 moves per second on M3 Max
🚨 YOUR MAC CAN MAKE AI DECISIONS IN 7.4 MS. A new Apple Silicon port brings Laya’s typed-decision AI to MLX — fully local. This is Laya-MLX. Instead of generating paragraphs token by token, it makes typed decisions directly. → `choice` → probabilities across options → `score` → probabilities across rubric levels → `noul` → probability of true/false → 0 output tokens → Runs natively on Apple Silicon → No PyTorch, Transformers runtime, or cloud API → 7.4 ms median on the multilingual checkpoint → 13.4 ms median on the English checkpoint And there’s a seriously cool demo: Laya plays Snake locally. Every move is a real model decision. The project reports 75.40 moves/second across 2,400 moves, with zero deaths in its paired M3 Max test. Basically: State → typed decision → action. No giant generated answer. Just the decision your software actually needs. One important detail: this is an independent MLX port, not an official Convai Innovations release. 🔗 #AI #AppleSilicon #MLX #LLM #MachineLearning #LocalAI #OpenSource #Python #AIAgents #Developers
Project: laya-mlx→
laya-mlx delivers typed decisions in 7–14 ms on an M3 Max
Vous vous êtes déjà retrouvé confronté à des décisions rapides qui nécessitent une inférence rapide et efficace ? Découvrez Laya-mlx, un environnement d'exécution MLX natif pour les modèles de décision typés Laya – des décisions courtes de 7 à 14 ms sur M3 Max. Sans génération de texte ni PyTorch ou API cloud, il offre des décisions typées à poids ouvert exécutées nativement sur Apple Silicon.
Project: laya-mlx→
laya-mlx makes typed decisions in 7 ms on Apple Silicon
🤖 laya-mlx ⭐ 6,374 stars Make local AI decisions in just 7ms on Apple Silicon. No PyTorch, no cloud APIs—just ultra-fast, native MLX speed for typed models. 🔗 #AI #MachineLearning
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