Onde Inference CLI

Onde Inference CLI

Command-line interface for Onde Inference.

Swift · Flutter · React Native · Rust · Website

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Manage your Onde Inference account, fine-tune local models, and export them to GGUF, all from the terminal.

Install

Install onde-cli with your favorite tool. For package docs and the full install matrix, see https://ondeinference.com/cli.

npm

npm install -g @ondeinference/cli

Homebrew

brew tap ondeinference/homebrew-tap && brew trust --tap ondeinference/homebrew-tap
brew install onde

pip / uv / uvx

pip install onde-cli
# or
uv tool install onde-cli
uv run onde --tui
# or with
uvx --from onde-cli onde --tui

.NET tool

dotnet tool install --global Onde.Cli

Dart pub global

dart pub global activate onde_cli

The Dart package is a thin launcher. On first run it downloads the right native binary into ~/.onde/cli, then reuses the local copy.

Pre-built binary

Download a release from GitHub Releases:

# macOS Apple Silicon
curl -Lo onde https://github.com/ondeinference/onde-cli/releases/latest/download/onde-macos-arm64
chmod +x onde && mv onde /usr/local/bin/onde

Platform	File
macOS Apple Silicon	onde-macos-arm64
macOS Intel	onde-macos-amd64
Linux x64	onde-linux-amd64
Linux arm64	onde-linux-arm64
Windows x64	onde-win-amd64.exe
Windows arm64	onde-win-arm64.exe

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Usage

onde --tui

This opens the TUI. Plain onde prints usage and exits. You can sign up or sign in right there.

Key	What it does
Tab	Move between fields
Enter	Submit or sign out
Ctrl+L	Go to the sign-in screen
Ctrl+N	Go to the new account screen
Ctrl+C	Quit

MCP server

Run onde as a Model Context Protocol server over stdio instead of the TUI:

onde --mcp

This exposes Onde account and model-catalog operations as MCP tools — login, me, apps_list, app_create, app_rename, models_list, model_register, model_assign, hf_search — returning structured JSON. stdout is the JSON-RPC channel; tools run non-interactively and reuse the token from a TUI sign-in (or the login tool). Point any MCP client at the command onde --mcp.

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Fine-tuning

onde includes a LoRA fine-tuning pipeline for Qwen2, Qwen2.5, and Qwen3 models. It runs locally: Metal on Apple Silicon, CPU elsewhere. No cloud setup. No Python environment.

The flow is straightforward: download a safetensors base model, fine-tune it with LoRA, merge the adapter back into the base weights, then export to GGUF for use in the Onde SDK.

If you want a quick refresher on what the model is actually doing at inference time, Onde has a short note on the forward pass.

Training data format

Each line should be one complete conversation in Qwen's chat template:

{"text": "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nWhat is LoRA?<|im_end|>\n<|im_start|>assistant\nLoRA adds small trainable matrices to frozen layers, letting you fine-tune large models without updating all the weights.<|im_end|>"}

Save the file wherever you want. The TUI lets you point to it directly.

Running it

onde --tui
  → Models tab (Tab from Apps)
  → Select a safetensors model (↑↓, Enter)
  → Press f

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

Configure the run:

Field	Default	Notes
Training data	~/.onde/finetune/train.jsonl	Path to your JSONL file
LoRA rank	8	Higher means more capacity and more memory use
Epochs	3	Full passes over the dataset
Learning rate	0.0001	AdamW default

Press Enter to start. In a healthy run, loss usually starts dropping by epoch 2. If it stays flat, try 0.0003.

After training

For rank 8 on a 0.6B model, the adapter is about 1.5 MB. From the fine-tune complete screen:

• m to merge the adapter into the base model
• g to export the merged model to GGUF

The resulting GGUF loads directly in the Onde SDK for on-device AI inference.

Supported base models

Model	Size	Notes
Qwen/Qwen3-0.6B	~1.2 GB	Smallest and quickest to train
Qwen/Qwen2.5-1.5B-Instruct	~3.0 GB	Good default for instruction tuning
Qwen/Qwen3-1.7B	~3.4 GB	Newer small Qwen3 model
Qwen/Qwen3-4B	~8.0 GB	Best quality, better suited to macOS

You can search for any of these from the Models tab with /.

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Debug

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

If you installed through pub.dev, the launcher cache lives under ~/.onde/cli.

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License

Dual-licensed under MIT and Apache 2.0.

Copyright

© 2026 Splitfire AB (Onde Inference).