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What you can do with Laya MLX

laya-mlx is a Python runtime that runs Laya typed decision models natively on Apple Silicon using MLX, with no text generation, PyTorch or cloud API.

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mizorewww/laya-mlx on GitHub

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What do you want to do?

4 use cases

  • Route support tickets to a team on your Mac

    Pick the right team for each ticket with a local typed choice decision.

    View prompt
    In the laya-mlx repo (github.com/mizorewww/laya-mlx), write a Python script that loads aac6fef/laya-mlx and routes the ticket text in <ticket_file> to one of the teams billing, technical or sales using a choice question. Print the answers dictionary. Verify that the printed label is one of the three criteria.

  • Answer several typed questions about one message

    Get a team choice, an urgency score and a refund yes/no flag in one call.

    View prompt
    In the laya-mlx repo (github.com/mizorewww/laya-mlx), write a script that loads aac6fef/laya-mlx and asks three questions about the message in <message_file>: a choice for department, a score for urgency with three rubric levels, and a noul for whether the customer asks for money back. Print the answers. Verify that the urgency value falls between 0 and 2.

  • Watch a local Snake agent make live decisions

    Run the terminal Snake demo where Laya picks every move on your Mac.

    View prompt
    In the laya-mlx repo (github.com/mizorewww/laya-mlx), install the demo extra and start the laya-snake terminal game in a terminal of at least 104 by 35 cells. Report the moves per second shown and whether any safety intervention occurred. Verify that the game runs without an external API key or network call during play.

  • Serve Laya over HTTP for internal apps

    Run a local Laya HTTP service that other internal tools can call.

    View prompt
    In the laya-mlx repo (github.com/mizorewww/laya-mlx), install the serve extra and start laya-mlx-serve on this Mac. Find the request format in the upstream Laya documentation and send one choice question. Verify that the service returns an answers field with probabilities for the named options.

How to use Laya MLX

  1. Check you have what it needs: An Apple Silicon Mac running macOS 14 or later; Python 3.11 or later; Internet access for the first checkpoint download, after which inference is fully local.
  2. Open mizorewww/laya-mlx on GitHub and read the README and license.
  3. Install the package on an Apple Silicon Mac: pip install laya-mlx
  4. In a Python file, import the library and load the checkpoint: import laya_mlx as laya then agent = laya.load("aac6fef/laya-mlx")
  5. Define a choice question with the criteria billing, technical and sales, as shown in the Quick start.
  6. Call agent.predict with the ticket text and the question dictionary.

These steps come from our top use case, Route support tickets to a team on your Mac, which also has a copy-paste agent prompt.

Laya MLX alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • An Apple Silicon Mac running macOS 14 or later
  • Python 3.11 or later
  • Internet access for the first checkpoint download, after which inference is fully local
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Questions about Laya MLX

What is Laya MLX?

laya-mlx is a Python runtime that runs Laya typed decision models natively on Apple Silicon using MLX, with no text generation, PyTorch or cloud API. It is an open-source project on GitHub: https://github.com/mizorewww/laya-mlx.

Who is Laya MLX for?

Python developers on Apple Silicon Macs who want local typed choice, score and yes/no decisions without a cloud API.

What can I do with Laya MLX?

4 use cases so far, for example: Route support tickets to a team on your Mac; Answer several typed questions about one message; Watch a local Snake agent make live decisions. Each has steps and a copy-paste agent prompt.

What do I need to run Laya MLX?

An Apple Silicon Mac running macOS 14 or later; Python 3.11 or later; Internet access for the first checkpoint download, after which inference is fully local.

What are alternatives to Laya MLX?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is Laya MLX safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.