Item Model Command

Set custom models using the new model system through a simple command

70 Downloads

fabricadventureequipmentutility

Rent Server with this Mod

About this Mod

Use the command format /item model "namespace:item_name"

You will be able to find the namespace of the pack inside the resource pack folder, along with the item name.

This does NOT use custom model data.

Example:
/item model grouphardcore:penguin_sword

Example file structure associated with the example command

Available Versions

Item Model Command 1.0.0release

MC 1.21.8fabric

September 22, 2025

How to Install Item Model Command on Your Server

Order Server

Order a Minecraft Java server with at least 3 GB RAM (4 GB recommended).

Set fabric Loader

In the panel under "Egg", select the fabric loader and matching Minecraft version (1.21.8).

Install Mod

Open the mod browser in the dashboard and search for "Item Model Command". Click "Install" – done! Alternatively, upload the .jar via SFTP to the /mods folder.

Compatibility

Mod Loaders

fabric

Minecraft Versions

1.21.8

Server-side

✓ Required

Recommended RAM

4 GB(min. 3 GB)

Frequently Asked Questions

Item Model Command server crashes on startup – what to do?

Most common cause: wrong fabric version or insufficient RAM. Check the server log (latest.log) for "OutOfMemoryError" or "Mixin" errors. With Mado Hosting: ensure at least 3 GB RAM is allocated and the loader matches the mod version (1.21.8). You can switch loaders with one click in the panel.

Is Item Model Command compatible with fabric?

Item Model Command officially supports fabric for Minecraft 1.21.8. The Mado dashboard automatically detects incompatible loader combinations.

Server lagging with Item Model Command – how to optimize performance?

Recommended RAM: 4 GB (per 8 players). Use /spark profiler to check if Item Model Command consumes the most tick time. Common fixes: reduce server view-distance to 8-10, install "performant" or "starlight" as supplementary mods on Forge. With Mado Hosting, your server runs on NVMe SSDs with dedicated CPU cores for minimal latency.