Onde Inference CLI

Command-line interface for Onde Inference.

Swift · Flutter · React Native · Rust · Website

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`

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.

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 /.

Debug

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

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

License

Dual-licensed under MIT and Apache 2.0.

Copyright

Onde Inference CLI

Command-line interface for Onde Inference.

Swift · Flutter · React Native · Rust · Website

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`

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.

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 /.

Debug

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

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

License

Dual-licensed under MIT and Apache 2.0.

Copyright

Onde Inference CLI

Command-line interface for Onde Inference.

Swift · Flutter · React Native · Rust · Website

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`

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.

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 /.

Debug

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

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

License

Dual-licensed under MIT and Apache 2.0.

Copyright

Onde Inference CLI

Command-line interface for Onde Inference.

Swift · Flutter · React Native · Rust · Website

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`

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.

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 /.

Debug

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

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

License

Dual-licensed under MIT and Apache 2.0.

Copyright

Onde Inference CLI

Command-line interface for Onde Inference.

Swift · Flutter · React Native · Rust · Website

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`

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.

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 /.

Debug

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

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

License

Dual-licensed under MIT and Apache 2.0.

Copyright

Onde Inference CLI

Command-line interface for Onde Inference.

Swift · Flutter · React Native · Rust · Website

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`

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.

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 /.

Debug

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

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

License

Dual-licensed under MIT and Apache 2.0.

Copyright

Onde Inference CLI

Command-line interface for Onde Inference.

Swift · Flutter · React Native · Rust · Website

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`

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.

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 /.

Debug

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

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

License

Dual-licensed under MIT and Apache 2.0.

Copyright

Onde Inference CLI

Command-line interface for Onde Inference.

Swift · Flutter · React Native · Rust · Website

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`

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.

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 /.

Debug

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

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

License

Dual-licensed under MIT and Apache 2.0.

Name		Name	Last commit message	Last commit date
.agents		.agents
.github/workflows		.github/workflows
.nuget/NuGet		.nuget/NuGet
assets		assets
docs		docs
npm		npm
nuget		nuget
pub/onde_cli		pub/onde_cli
pypi		pypi
src		src
.gitignore		.gitignore
.nvmrc		.nvmrc
AGENTS.md		AGENTS.md
CLAUDE.md		CLAUDE.md
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
LICENSE		LICENSE
README.md		README.md
build.rs		build.rs
rust-toolchain.toml		rust-toolchain.toml
server.json		server.json

Name		Name	Last commit message	Last commit date
.agents		.agents
.github/workflows		.github/workflows
.nuget/NuGet		.nuget/NuGet
assets		assets
docs		docs
npm		npm
nuget		nuget
pub/onde_cli		pub/onde_cli
pypi		pypi
src		src
.gitignore		.gitignore
.nvmrc		.nvmrc
AGENTS.md		AGENTS.md
CLAUDE.md		CLAUDE.md
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
LICENSE		LICENSE
README.md		README.md
build.rs		build.rs
rust-toolchain.toml		rust-toolchain.toml
server.json		server.json

Name		Name	Last commit message	Last commit date
.agents		.agents
.github/workflows		.github/workflows
.nuget/NuGet		.nuget/NuGet
assets		assets
docs		docs
npm		npm
nuget		nuget
pub/onde_cli		pub/onde_cli
pypi		pypi
src		src
.gitignore		.gitignore
.nvmrc		.nvmrc
AGENTS.md		AGENTS.md
CLAUDE.md		CLAUDE.md
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
LICENSE		LICENSE
README.md		README.md
build.rs		build.rs
rust-toolchain.toml		rust-toolchain.toml
server.json		server.json

Name		Name	Last commit message	Last commit date
.agents		.agents
.github/workflows		.github/workflows
.nuget/NuGet		.nuget/NuGet
assets		assets
docs		docs
npm		npm
nuget		nuget
pub/onde_cli		pub/onde_cli
pypi		pypi
src		src
.gitignore		.gitignore
.nvmrc		.nvmrc
AGENTS.md		AGENTS.md
CLAUDE.md		CLAUDE.md
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
LICENSE		LICENSE
README.md		README.md
build.rs		build.rs
rust-toolchain.toml		rust-toolchain.toml
server.json		server.json

Name		Name	Last commit message	Last commit date
.agents		.agents
.github/workflows		.github/workflows
.nuget/NuGet		.nuget/NuGet
assets		assets
docs		docs
npm		npm
nuget		nuget
pub/onde_cli		pub/onde_cli
pypi		pypi
src		src
.gitignore		.gitignore
.nvmrc		.nvmrc
AGENTS.md		AGENTS.md
CLAUDE.md		CLAUDE.md
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
LICENSE		LICENSE
README.md		README.md
build.rs		build.rs
rust-toolchain.toml		rust-toolchain.toml
server.json		server.json

Uh oh!

Latest commit

History

Folders and files

Repository files navigation

Onde Inference CLI

Install

npm

Homebrew

pip / uv / uvx

.NET tool

Dart pub global

Pre-built binary

Usage

MCP server

Fine-tuning

Training data format

Running it

After training

Supported base models

Debug

License

Copyright

About

Topics

Resources

Stars

Watchers

Forks

Releases

Contributors

Languages

Uh oh!

Latest commit

History

Folders and files

Repository files navigation

Onde Inference CLI

Install

npm

Homebrew

pip / uv / uvx

.NET tool

Dart pub global

Pre-built binary

Usage

MCP server

Fine-tuning

Training data format

Running it

After training

Supported base models

Debug

License

Copyright

About

Topics

Resources

Stars

Watchers

Forks

Releases

Contributors

Languages

Uh oh!

Latest commit

History

Folders and files

Repository files navigation

Onde Inference CLI

Install

npm

Homebrew

pip / uv / uvx

.NET tool

Dart pub global

Pre-built binary

Usage

MCP server

Fine-tuning