UMR

The Unified Model Registry for your local AI apps.

Get Started ⋅ Docs ⋅ GitHub

UMR allows you to maintain a single, centralized copy of a model to use across your favorite local AI apps, instead of having each one manage a separate copy.

That means you can:

• Save disk space
• Use the same model across all of your apps instantly
• Manage all your local models in one place

Install

Install UMR via NPM or your JS package manager of choice.

npm i -g umr-cli

The umr CLI will be available after installation.

Getting Started

Get started by adding a model to the UMR-maintained registry.

# Add a model from Hugging Face
# You will be prompted to choose a quant version
# This will use HF Cache, but UMR will now know about it
umr add hf ggml-org/gemma-4-E2B-it-GGUF

# Add a GGUF file manually
# This will make a copy of the GGUF to UMR's own store
umr add ./gemma-4-E2B-it-q8-0.gguf

After adding, check your available models

# Output depends on which quant you chose
umr list


# NAME                 SOURCE  FORMAT  SIZE     CLIENTS    STATUS
# gemma-4-e2b-it-q8-0  hf      gguf    4.63 GB  -          ok

Now you can use the model in all your favorite apps right away. umr link is lightning fast, and the model should appear immediately in the linked app.

# Link the model to LM Studio
umr link lmstudio gemma-4-e2b-it-q8-0

# Link the model to Ollama
umr link ollama gemma-4-e2b-it-q8-0

# Link the model to Jan
umr link jan gemma-4-e2b-it-q8-0

Alternatively, you can also get the raw GGUF path to use with other AI runtimes

# Get the path to the GGUF
umr show gemma-4-e2b-it-q8-0 --path

# Run it with llama.cpp, for example
llama-cli -m "$(umr show gemma-4-e2b-it-q8-0 --path)"

See GitHub for full documentation.