Ollama — run language models from your terminal

$ ollama list

NAME                       ID              SIZE      MODIFIED     
smollm2:360m               297281b699fc    725 MB    3 days ago

Your local model shelf: NAME, ID, SIZE on disk, MODIFIED. The 360m tag means 360 million parameters — small enough to run on a decade-old laptop. The ID is a git-style content hash; two entries sharing an ID are the same weights. A cloud-pointer model shows a dash in SIZE, because it lives in the registry, not on your disk.

$ ollama pull smollm2:360m

pulling manifest 
pulling 7d23be4097d6: 100% ▕██████████████████▏ 725 MB
pulling fbacade46b4d: 100% ▕██████████████████▏   68 B
pulling d502d55c1d60: 100% ▕██████████████████▏   675 B
verifying sha256 digest 
writing manifest 
success 

Layers, exactly like docker pull: the weights blob, the template, the license, each with its own digest, verified by sha256. Storage is content-addressed, so a variant that shares most layers with a model you already have downloads in seconds.

$ ollama run smollm2:360m "Why is the sky blue? Answer in one short sentence."

The sky appears blue because of a phenomenon called Rayleigh scattering, where shorter wavelengths of light are scattered more than longer wavelengths, giving the impression of blue.

One-shot mode: prompt as argument, answer to stdout, exit. On a fresh model the first run downloads it first — the official quickstart demos exactly this with gemma4:e2b — so pull and run are the same step when convenient. No API key, ever.

$ ollama run smollm2:360m "Name one thing octopuses can do." --verbose

One thing an octopus can do is change its skin texture, color, and shape to adapt to their surroundings, prey, or predators.

total duration:       1.267192095s
load duration:        1.376343ms
prompt eval count:    41 token(s)
prompt eval duration: 70.878ms
prompt eval rate:     578.46 tokens/s
eval count:           28 token(s)
eval duration:        1.190171s
eval rate:            23.53 tokens/s

The honest stopwatch. eval rate is generation speed in tokens per second, prompt eval rate is how fast the model reads your input, load duration tells you whether it was already sitting in memory. Benchmark models or hardware with these numbers, never with vibes.

$ ollama run smollm2:360m "Give me a JSON object with keys 'animal' and 'sound' for a cat." --format json

{
  "animal": "cat",
  "sound": "meow"
}

--format json steers the output into a JSON object — this 360-million-parameter model nailed the schema on the first try. Pair it with --think=false on reasoning models when you want the answer without the visible thinking.

$ ollama run smollm2:360m

>>> Send a message (/? for help)

No prompt argument drops you into the interactive REPL: /bye quits, /? lists the slash commands, /set system changes the system prompt mid-session, /show info prints the model card. Conversation memory lives for the session only — exit and it is gone.

$ ollama ps

NAME             ID              SIZE      PROCESSOR    CONTEXT    UNTIL              
smollm2:360m     297281b699fc    919 MB    100% CPU     4096       4 minutes from now

The running-model table. PROCESSOR shows where the model landed (100% CPU on this box, GPU+CPU splits on mixed systems), CONTEXT is the loaded context length, UNTIL counts down the keepalive — 5 minutes by default — after which the memory is freed. This is also your first stop when disk or VRAM looks surprisingly busy.

$ ollama run no-such-model-xyz "hi"

pulling manifest ⠋ pulling manifest ⠙ ... 
Error: pull model manifest: file does not exist

The classic first error. A typo, or a name that does not exist in the library, fails after a few spinner turns of manifest checking. Note what Ollama tried first: it attempted to pull it before complaining. When this happens, check spelling against ollama.com/library or run ollama list to see what you actually have.

$ printf 'FROM smollm2:360m\nSYSTEM """You are a pirate. Reply in pirate speak."""' > Modelfile
ollama create pirate-helper -f Modelfile

gathering model components 
using existing layer sha256:7d23be40...
creating new layer sha256:f1b3345a34bb...
writing manifest 
success 

A Modelfile is a Dockerfile for a personality. FROM picks the base weights, SYSTEM sets the standing instruction, PARAMETER lines tune generation settings — and ollama create bakes it into a new entry. Real output, abridged: three layers reused from the base, two new ones for the recipe. ollama show pirate-helper then prints back your system prompt, temperature, and the base model's stats.

$ ollama cp pirate-helper pirate-helper-backup && ollama rm pirate-helper-backup

copied 'pirate-helper' to 'pirate-helper-backup' 
deleted 'pirate-helper-backup' 

cp duplicates a model entry — it copies the manifest, not 725 MB, so both names share the same weights — and rm removes entries. rm on a model you created releases its unique recipe layers; the base model stays on disk because the original still references it. That is why rm so often frees less space than people expect: layers go only when nothing points at them anymore.