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/run-llms

@e0627a9
by Ivan Charapanauav/skills16 stars
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Set up and run local LLMs with Harbor. Use when the user wants to run models locally, install Harbor, start Open WebUI, llama.cpp, Ollama, vLLM, Docker Model Runner, MLX, or oMLX, pull GGUF or HuggingFace models, add SearXNG web search, Speaches TTS/STT, or Open Terminal code execution, launch Codex/Claude/Grok/OpenCode against a Harbor backend, or troubleshoot GPU, VRAM, and service startup.

Use this Skill: https://skilld.dev/gh/av/skills/run-llms

This session only. Nothing lands on disk.

referencesmodels.md

≈717 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Models

Primary commands: harbor pull and harbor models. harbor pull accepts a service handle (Compose image pull) or a model spec. If any non-flag argument is not a known service, Harbor treats the invocation as a model pull.

Routing (harbor pull / harbor models pull)

When --source is omitted:

  1. Query the Hugging Face Hub API for org/repo (the part before an optional :tag, 5s timeout).
  2. Repo missing → Ollama pull.
  3. Repo exists and publishes .gguf → ephemeral llama.cpp download into the llama.cpp cache.
  4. Repo exists with no GGUF → Hugging Face CLI into the HF cache.

harbor pull does not accept --source. Use harbor models pull --source for DMR, MLX, or oMLX.

# Ollama registry
harbor pull qwen3.5:4b
harbor pull llama3.2:3b

# HuggingFace via Ollama
harbor pull hf.co/bartowski/gemma-2-2b-it-GGUF:Q4_K_M

# GGUF → llama.cpp cache (router discovers it)
harbor pull unsloth/Qwen3.5-4B-GGUF:Q4_K_M
harbor pull microsoft/Phi-3.5-mini-instruct-gguf

# safetensors → HF cache (vLLM, TGI, etc.)
harbor pull Qwen/Qwen3.5-4B

# Host backends
harbor models pull --source dmr ai/smollm2
harbor models pull --source mlx mlx-community/Qwen3.5-4B-4bit
harbor models pull --source omlx mlx-community/Qwen3.5-4B-4bit

Aliases: harbor models dmr pull ..., harbor models mlx pull ..., harbor models omlx pull ....

GGUF pulls start an ephemeral llama.cpp container. Custom GPU images (ROCm/Vulkan toolboxes) can fail in that container because it runs with --n-gpu-layers 0 and no device access. Temporarily point the active capability image at the official CPU server, pull, then restore:

harbor config set llamacpp.image.rocm ghcr.io/ggml-org/llama.cpp:server
harbor pull unsloth/Qwen3.5-4B-GGUF:Q4_K_M
harbor config set llamacpp.image.rocm <original-image>

List and remove

harbor models ls
harbor models ls --json
harbor models ls --source llamacpp
harbor models ls --source dmr

harbor models rm qwen3.5:4b
harbor models rm unsloth/Qwen3.5-4B-GGUF
harbor models rm unsloth/Qwen3.5-4B-GGUF:Q4_K_M
harbor models rm --source dmr ai/smollm2

Sources: ollama (skipped if Ollama is down), hf (HF hub cache), llamacpp (GGUF cache), dmr / mlx / omlx (queried by default only when their Harbor proxy is running; --source hits the host endpoint directly).

HuggingFace helpers

harbor hf scan-cache
harbor hf token                 # show
harbor hf token <token>         # set (gated models)
harbor hf cachedir
harbor hf cachedir /path/to/cache
harbor hf path user/repo
harbor hf download user/repo
harbor hf download user/repo file.gguf
harbor hf find gguf gemma
harbor hf parse-url https://huggingface.co/user/repo/blob/main/file.gguf

hf_transfer is enabled for faster HF downloads. Disk use: harbor size. Find a GGUF on disk: harbor find .gguf.

Source: SKILL.md on GitHub

1 alert1d4 checks · Risk HIGH
  • Gen Agent Trust Hub1d

    This skill provides a playbook for managing local LLM stacks using Harbor. It includes instructions to download and execute setup scripts and agent instructions directly from the vendor's GitHub repository, uses administrative commands for Docker configuration, and manages various local services via shell commands.

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    Risk: MEDIUM · 1 issue

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Signed by skilld at e0627a9. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub last week.

Activeupdated 4 weeks ago

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