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Readme

@ondeinference/cli

A terminal app for your Onde Inference account, plus local model fine-tuning.

Install

npm install -g @ondeinference/cli

npm installs the right native binary for your platform automatically.

It works on:

macOS (Apple Silicon and Intel)
Linux (x64 and arm64)
Windows (x64 and arm64)

Other ways to install

Method	Command
Homebrew	`brew install ondeinference/homebrew-tap/onde`
pip	`pip install onde-cli`
uv	`uv tool install onde-cli`
Dart pub	`dart pub global activate onde_cli`
.NET tool	`dotnet tool install --global Onde.Cli`
Cargo	`cargo install onde-cli`

Run it

onde

That opens the terminal UI.

From there you can:

sign up or sign in
create and manage apps
assign models
fine-tune supported local models
export merged models to GGUF

No browser needed.

Basic keys

Key	Action
`Tab`	Move between fields
`Enter`	Submit or confirm
`Ctrl+L`	Go to sign in
`Ctrl+N`	Go to create account
`Ctrl+C`	Quit

Fine-tuning

onde can fine-tune Qwen2, Qwen2.5, and Qwen3 safetensors models with LoRA.

Training runs locally:

Metal on Apple Silicon
CPU on other platforms

So yes, no cloud training setup and no Python environment to babysit.

If you want a quick mental model for what the network is doing once it starts running, Onde has a short write-up on the forward pass.

Supported base models

Model	Size
`Qwen/Qwen3-0.6B`	~1.2 GB
`Qwen/Qwen2.5-1.5B-Instruct`	~3.0 GB
`Qwen/Qwen3-1.7B`	~3.4 GB
`Qwen/Qwen3-4B`	~8.0 GB

Only safetensors models can be fine-tuned. GGUF models are already quantized, so their weights are not differentiable.

Training data

Use one JSON object per line. Each object needs a text field containing the full conversation in Qwen's chat template.

{"text": "<|im_start|>user\nWhat is the boiling point of water?<|im_end|>\n<|im_start|>assistant\n100°C at sea level.<|im_end|>"}

Running a fine-tune

Open the Models tab.
Pick a safetensors model with ↑ / ↓.
Press f to open the fine-tune config.
Set your data path, LoRA rank (default 8), epochs (default 3), and learning rate (default 0.0001).
Start training.

A rank-8 adapter for the 0.6B model is about 1.5 MB, so the output stays pretty small.

After training

Press m to merge the adapter into the base weights.
Press g to export the merged model to GGUF.

The exported file loads directly in the Onde SDK for on-device inference.

What is Onde?

Onde Inference is for running LLMs on the user's device. No server round-trips, no sending prompts off to somebody else's machine.

It ships as native SDKs for:

Swift · Flutter · React Native · Rust

The CLI is for account management and local fine-tuning. The SDKs are what you ship in your app.

Debug

Logs are written to ~/.cache/onde/debug.log.

License

Dual-licensed under MIT and Apache 2.0.