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Quickstart

Get ModelDock running in 30 seconds.

Prerequisites

  • Python 3.9+
  • A local Ollama install with the daemon running (ollama serve)

Install

pip install modeldock
# with the Ollama backend helper (optional):
pip install modeldock[ollama]

30-Second Start

import modeldock as md

# Auto-installs if missing, then returns a ready-to-use client
client = md.load("llama3")

# Use it (runtime-native client, e.g. ollama.Client)
print(client.chat(model="llama3", messages=[{"role": "user", "content": "Hi!"}]))

That's it. No terminal commands, no searching model names.

How load() Works

Model requested | Already installed? ──yes──> return client | no Download automatically (with progress) | Verify installation (checksum + runtime check) | Load model -> return ready client

Set auto_install=True in config to enable automatic downloads, or pass backend="ollama" to target a specific runtime.

Common Commands

import modeldock as md

md.list()                       # browse the catalog
md.search("coding")             # search by name / capability / category
md.installed()                  # what's already local
md.info("qwen3")                # sizes, capabilities, variants
md.recommend(task="vision")     # guided pick

md.install("llama3")            # explicit download
md.install_category("coding")   # bulk install
md.update("llama3")             # pull newer tag
md.remove("llama3")             # uninstall
md.verify("llama3")             # integrity check

md.cache.status()               # what's cached
md.cache.clean()                # drop orphans / partials

modeldock load llama3
modeldock install llama3 qwen3 deepseek-r1
modeldock install-category coding
modeldock list
modeldock installed
modeldock search vision
modeldock info qwen3
modeldock update llama3
modeldock remove llama3
modeldock cache status
modeldock cache clean
modeldock --help

Global flags: --backend, --config-path, --log-level, --no-progress, --yes.

Next Steps