---
name: Ollama Serving
slug: ollama-serving
category: AI Engineering
description: "Ollama Serving pulls and runs local models with Ollama, exposing an OpenAI-compatible endpoint on localhost:11434 for apps and agents. Use it to add a self-hosted model to PenguinHarness or any OpenAI client."
github: "https://github.com/Prism-Shadow/penguin-harness/tree/main/packages/skills/skills/ollama"
language: TypeScript
stars: 1506
forks: 150
install: "npx degit https://github.com/Prism-Shadow/penguin-harness/tree/main/packages/skills/skills/ollama ~/.claude/skills/ollama"
installs_to: ~/.claude/skills/ollama
source_path: packages/skills/skills/ollama/SKILL.md
collection_size: 21
category_size: 2451
collection_url: "https://dirskills.com/collections/Prism-Shadow/penguin-harness"
added: 2026-08-19T07:26:57.496Z
last_synced: 2026-08-19T07:26:57.496Z
canonical_url: "https://dirskills.com/skills/ollama-serving"
---

# Ollama Serving

Ollama Serving pulls and runs local models with Ollama, exposing an OpenAI-compatible endpoint on localhost:11434 for apps and agents. Use it to add a self-hosted model to PenguinHarness or any OpenAI client.

**Install:**

```bash
npx degit https://github.com/Prism-Shadow/penguin-harness/tree/main/packages/skills/skills/ollama ~/.claude/skills/ollama
```

## README

# Ollama Serving

Ollama runs open-weight models locally with automatic GPU detection and an OpenAI-compatible API on `http://localhost:11434`.

## Before you start

If the user's message only invokes this skill (e.g. "use ollama skill") without a concrete request, ask the user what they want. Do not run any command until the goal is clear.

Ask the user which model to run; if they have no preference, recommend the small default [Qwen/Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B) (`ollama pull qwen3.5:0.8b`). The model must fit the machine's RAM/VRAM.

Ollama runs everywhere — macOS, Linux and Windows, on CPUs as well as NVIDIA/AMD GPUs — so engine choice follows the user's preference: Ollama is the simple default, while vLLM targets high-throughput GPU serving. Check the current state first:

```bash
ollama --version   # is Ollama installed?
ollama ps          # is the service already serving models?
```

If port 11434 is already serving, reuse that instance — never kill an existing Ollama process.

## Suggested workflow

1. Ask the user which model to run; with no preference, recommend [Qwen/Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B) (`qwen3.5:0.8b`).
2. Pick the engine the user prefers: Ollama is the default; vLLM covers high-throughput GPU serving.
3. Install Ollama if missing, then `ollama pull qwen3.5:0.8b`.
4. Verify with `curl http://localhost:11434/v1/models`.
5. Register the endpoint: `penguin config model add ... --client-type openai --base-url http://localhost:11434/v1` — a pulled Ollama model is not visible to Penguin until added.
6. Confirm the new entry with `penguin config model list`.

## Install

```bash
curl -fsSL https://ollama.com/install.sh | sh   # Linux; macOS/Windows use the desktop app
```

The service then listens on `http://localhost:11434`.

## Pull and run

```bash
ollama pull qwen3.5:0.8b   # download a model
ollama run qwen3.5:0.8b    # interactive chat (pulls first if missing)
ollama list                # downloaded models
ollama ps                  # models loaded in memory
ollama stop qwen3.5:0.8b   # unload a model
```

## OpenAI-compatible endpoint

The endpoint is `http://localhost:11434/v1`; any non-empty API key is accepted (conventionally `ollama`):

```bash
curl http://localhost:11434/v1/models
```

## Context length

The default context window is small, and agent sessions need a large one. Raise it in the server's environment:

```bash
OLLAMA_CONTEXT_LENGTH=32768 ollama serve   # systemd service: set it via `systemctl edit ollama`
```

Or bake it into a model variant with a Modelfile:

```
FROM qwen3.5:0.8b
PARAMETER num_ctx 32768
```

```bash
ollama create qwen3.5-32k -f Modelfile
```

## Register with PenguinHarness

Model configuration is the penguin CLI's job — `penguin config model add` registers an endpoint and `penguin config model list` shows what has been registered. A pulled Ollama model is not visible to Penguin until you add it:

```bash
penguin config model add --provider custom --client-type openai \
  --base-url http://localhost:11434/v1 --model-id qwen3.5:0.8b --api-key ollama
penguin config model list   # the new entry should now be listed
```