Onde Inference MCP Server

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<h1 align="center">Onde Inference CLI</h1>

<p align="center"> Command-line interface for <a href="https://ondeinference.com/">Onde Inference</a>. </p>

<p align="center"> <a href="https://ondeinference.com"><img src="https://img.shields.io/badge/ondeinference.com-235843?style=flat-square&labelColor=17211D" alt="Website"></a> <a href="https://apps.apple.com/se/developer/splitfire-ab/id1831430993"><img src="https://img.shields.io/badge/App%20Store-live-235843?style=flat-square&labelColor=17211D" alt="App Store"></a> <a href="https://www.npmjs.com/package/@ondeinference/cli"><img src="https://img.shields.io/npm/v/@ondeinference/cli?style=flat-square&labelColor=17211D&color=235843" alt="npm"></a> <a href="https://pypi.org/project/onde-cli/"><img src="https://img.shields.io/pypi/v/onde-cli?style=flat-square&labelColor=17211D&color=235843" alt="PyPI"></a> <a href="https://pub.dev/packages/onde_cli"><img src="https://img.shields.io/pub/v/onde_cli?style=flat-square&labelColor=17211D&color=235843" alt="pub.dev"></a> <a href="https://www.nuget.org/packages/Onde.Cli"><img src="https://img.shields.io/nuget/v/Onde.Cli?style=flat-square&labelColor=17211D&color=235843" alt="NuGet"></a> <a href="https://crates.io/crates/onde-cli"><img src="https://img.shields.io/crates/v/onde-cli?style=flat-square&labelColor=17211D&color=235843" alt="Crates.io"></a> </p>

<p align="center"> <a href="https://github.com/ondeinference/onde-swift">Swift</a> · <a href="https://pub.dev/packages/onde_inference">Flutter</a> · <a href="https://www.npmjs.com/package/@ondeinference/react-native">React Native</a> · <a href="https://crates.io/crates/onde">Rust</a> · <a href="https://ondeinference.com">Website</a> </p>

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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
# or with
uvx --from onde-cli onde

.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

This opens the TUI. 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
  → 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).