OwnLLM Docs
CLI

recipes

Install, list, and delete model recipes — curated presets the site catalogues.

ownllm recipes list
ownllm recipes install <preset>
ownllm recipes delete <id>

A recipe is a curated bundle of:

  • A model tag (qwen2.5-coder:32b)
  • A runtime profile (auto or apple-mlx)
  • Default options (keep_alive, thinking, num_parallel)
  • Hardware requirements (min VRAM, RAM, recommended use case)
  • A licence flag

The site maintains the catalogue at /api/v1/recipes. The CLI fetches it on demand — no local copy to keep up to date.

list

ownllm recipes list

Prints every recipe the catalogue currently exposes, filtered to the ones your hardware can run (based on the heartbeat-reported specs):

PRESET                          MODEL                   PROFILE     STATUS
generalist-light                llama3.2:3b             auto        installed
code-mid                        qwen2.5-coder:14b       auto        available
code-heavy                      qwen2.5-coder:32b       auto        installed
apple-mlx-coder                 qwen3.5:35b-a3b-coding  apple-mlx   available
vision-mid                      qwen2-vl:7b             auto        unsupported (RAM)

install

ownllm recipes install code-heavy

Resolves the recipe → calls the equivalent of ownllm models install <model>, applies the recipe's options, and records the link locally so subsequent models list calls show the recipe id.

A recipe can ship default options (keep_alive: "30m", thinking: "low") so admins don't have to manually configure each model after install.

delete

ownllm recipes delete code-heavy

Calls ollama rm for the underlying model and removes the recipe metadata. The model is gone after this call returns.

Why recipes?

Recipes solve two problems:

  1. Curation. A new tenant doesn't have to know which Ollama tag to pull or what keep_alive to set. The site recommends the right model for the host's hardware.
  2. Profiles. On Apple Silicon, MLX-accelerated recipes show up when the agent reports accelerators: ["metal", "mlx"]. On x86_64 + NVIDIA, those recipes are filtered out and CUDA-friendly recipes are recommended instead.

See models if you want to install something that isn't in the catalogue.

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