ZelonAI ZelonAI

iOS App · iOS

A small model, understood from the inside.

MicroLLM

MicroLLM helps learners run, inspect, and modify small language model training workflows locally with embedded Python, structured lessons, and project sandboxes.

Preview MicroLLM See how it works About Coming Soon
Privacy-first No account needed Works offline
MicroLLM mark

MicroLLM

Status
Coming Soon
App Store
iOS
Tech Stack
SwiftUI · Embedded Python · Education

Features

No. 01

The training room, in your pocket.

01

On-device mini model runs with visible logs and metrics

02

Structured lessons from tokenizer to transformer blocks

03

Local project sandbox with editable Python workflows

Workflow

No. 02

One concept’s full lifecycle.

  1. 01

    Learn the principle

    The structured course explains the concept first: the problem it solves, what it looks like.

  2. 02

    Hands on in the sandbox

    Open the matching Python workflow in the project sandbox; change code, tune parameters, run locally.

  3. 03

    Observe and modify

    Watch the logs and metrics shift — understanding lands here, from “heard of” to “handled”.

Trust & Privacy

No. 03

Learn AI without surrendering your data.

Privacy-first

Local-first: no account, no ads, no tracking — data never leaves the device.

No account needed

No sign-up, no login. Install it and it just works.

Works offline

Everything core runs offline; no network required.

Still in the workshop — follow progress on the product page. Rather than memorizing terms, train a small model yourself.

About MicroLLM

No. 4

The longer story — what it is, who it is for, and the choices behind it.

Last Updated: March 13, 2026
Website: https://zelonai.com/app/microllm/

MicroLLM

MicroLLM is a mini model lab for iPhone and iPad.

It helps learners and builders run, inspect, and modify small language model workflows directly on-device, with embedded Python and structured learning paths.

Core Experience

  • Quick Run: Launch a mini model and watch logs, metrics, and generated samples
  • Learn by Building: Move from tokenizer and autograd to attention and transformer assembly
  • Editable Projects: Fork the reference project, inspect code, and run your own experiments
  • Local Workspace: Keep project files in an app sandbox and export only when you choose

Product Positioning

  • Local-first: The current release focuses on on-device experiments instead of cloud execution
  • Transparent runtime: Code, logs, tests, and project state are visible to the learner
  • Education + prototyping: Designed for teaching demos, self-study, and quick algorithm validation

Contact

© 2026 MicroLLM. All rights reserved.

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Docs & Support

No. 5

Plain-language documents. No fine print you would be surprised by.

MicroLLM About MicroLLM MicroLLM Privacy Policy MicroLLM Terms of Service MicroLLM Support Center

Next concept: train it yourself.

MicroLLM

In development — follow progress on the product page.

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