Ollama Keyboard Shortcuts

Complete Ollama keyboard shortcuts and commands reference — 29 shortcuts across 4 categories. Quick reference cheat sheet for Windows & Mac.

Model Management (7)

ShortcutAction
ollama pull <model>Download model
ollama pull <model>:<tag>Pull specific version
ollama listList local models
ollama rm <model>Remove model
ollama show <model>Model info
ollama cp <src> <dst>Copy model
ollama push <model>Publish model

Running & Inference (8)

ShortcutAction
ollama run <model>Run interactive
ollama run <model> "prompt"Single prompt
cat file.txt | ollama run <model>Pipe input
/byeExit chat
/clearClear history
/set system <text>Set system prompt
/show infoModel details
/show modelfileShow Modelfile

Server & API (8)

ShortcutAction
ollama serveStart server
OLLAMA_HOST=0.0.0.0 ollama serveExpose server
curl localhost:11434/api/generateGenerate API
curl localhost:11434/api/chatChat API
curl localhost:11434/api/tagsList models API
OLLAMA_MODELS=<path>Custom model dir
OLLAMA_NUM_PARALLEL=4Parallel requests
OLLAMA_MAX_LOADED_MODELS=2Max loaded models

Modelfile (6)

ShortcutAction
FROM <model>Base model
SYSTEM "<text>"System prompt
PARAMETER temperature 0.7Temperature
PARAMETER num_ctx 4096Context length
TEMPLATE "{{ .Prompt }}"Prompt template
ollama create <name> -f ModelfileBuild model
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The pull–run loop

Day-to-day Ollama is three commands: ollama pull <model> downloads, ollama run <model> opens an interactive chat, and ollama list shows what is on disk (ollama rm <model> reclaims the space — local models are gigabytes each). Tags select variants: ollama pull <model>:<tag> pins a size or quantisation instead of the default. For scripting, pass the prompt inline with ollama run <model> "prompt" or pipe a file straight in: cat file.txt | ollama run <model>.

Inside an interactive session, slash-commands control the chat: /clear wipes context, /set system <text> changes the system prompt on the fly, /show info and /show modelfile reveal what you are actually running, and /bye exits.

Serving and shaping models

ollama serve runs the API server that everything talks to on port 11434 — curl localhost:11434/api/generate and curl localhost:11434/api/chat are the two endpoints most integrations use. Environment variables tune it: OLLAMA_MODELS=<path> relocates the model store to a bigger drive, OLLAMA_NUM_PARALLEL=4 allows concurrent requests, and OLLAMA_HOST=0.0.0.0 ollama serve exposes the server to your network. To make a customised assistant permanent, write a Modelfile — FROM <model>, a SYSTEM "<text>" prompt, PARAMETER temperature 0.7 and PARAMETER num_ctx 4096 — then build it once with ollama create <name> -f Modelfile and run it by name forever after.

Frequently asked questions

What do the tags after the colon mean?

Variants of the same model: parameter sizes and quantisation levels. Pulling without a tag gets the default; ollama pull <model>:<tag> pins an exact one. ollama show <model> reports what a downloaded copy actually is — worth checking before comparing quality or speed.

Is OLLAMA_HOST=0.0.0.0 safe?

Treat it carefully. It binds the server to every network interface with no authentication of its own, so anyone who can reach the port can run your models. Fine on a trusted LAN; behind anything less, keep the default localhost binding or put a reverse proxy with auth in front.

Where do the models live, and can I move them?

In Ollama's model directory under your user profile by default. Set OLLAMA_MODELS=<path> (for the server process) to store them elsewhere — a common fix when the system drive is small, since a handful of models easily consumes tens of gigabytes.

Do I need to run ollama serve myself?

Usually not on a desktop install, where the background service starts automatically and ollama run just works. Run ollama serve manually on headless machines, in containers, or whenever you want to control the environment variables the server starts with.

How do I make a custom assistant I can reuse?

Write a Modelfile: FROM <model> for the base, SYSTEM "<text>" for standing instructions, PARAMETER lines for behaviour. Then ollama create <name> -f Modelfile builds it as a named local model — your settings persist without repeating /set system every session.

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