Local Models

local-models

Quick access to local LLMs through llama.cpp, reusing the GGUF models already pulled by Ollama (no re-download for text and embeddings). Everything runs on the machine — no API key, no network, no per-token cost.

When to use this skill

Reach for local models instead of a cloud API when the task is:

• Privacy-sensitive — redacting PII, processing personal notes, health data, secrets-adjacent text. The data never leaves the machine.
• Offline — no network available, or the user explicitly wants local-only.
• High-volume / low-stakes — classifying or tagging hundreds of items, where a small model is good enough and cloud cost/latency would add up.
• A fast throwaway — a quick summary, translation, or "what is this" where round-tripping to a frontier model is overkill.

Prefer a frontier (Claude) model when the task needs strong reasoning, long context, careful code, or high accuracy — these local models are small (0.6–4B).

The core trick: reuse Ollama's models

Ollama stores model weights as extension-less GGUF blobs under ~/.ollama/models/blobs/. These are ordinary GGUF files — llama.cpp loads them directly. scripts/ollama_blob.py reads Ollama's manifests and resolves a friendly name (e.g. qwen2.5:3b) to its weights blob path. No conversion, no duplicate downloads.

Usage

The entry point is scripts/lm. Run scripts/lm help for the full list. Invoke it with an absolute path, e.g. ~/ai_projects/claude-skills/local-models/scripts/lm.

lm models                       # list local models (text / vision / embed)
lm ask [MODEL] "PROMPT"         # one-shot prompt (default qwen2.5:3b)
lm chat [MODEL]                 # interactive REPL

# Text presets — accept a file path, inline text, OR stdin:
lm summarize  report.md
cat notes.txt | lm tldr
lm keywords   article.txt
lm anonymize  transcript.txt         # → [NAME] [EMAIL] [PHONE] [ADDRESS] ...
lm proofread  draft.md
lm translate  German "Good morning"
lm classify   "praise,complaint,question"  feedback.txt   # → one label
lm extract    "invoice_number, total, due_date"  invoice.txt   # → JSON

# Vision (downloads model+projector once via HuggingFace — see note below):
lm describe-image photo.jpg
lm tag-image      screenshot.png
lm vision photo.jpg "What brand is the shoe?"

# Embeddings & serving:
lm embed "text to embed"             # → OpenAI-style JSON vector
lm serve qwen2.5:3b 8080             # OpenAI-compatible server on :8080

Output is clean (just the answer) — the wrapper drives llama-completion in single-turn mode and strips the chat-template scaffolding and llama.cpp logs.

Choosing a model

Defaults are tuned for clean, fast output and can be overridden per call:

• General text presets → qwen2.5:3b (LM_TEXT_MODEL)
• Classify / extract → qwen2.5:3b (LM_REASON_MODEL), run at temperature 0
• Embeddings → jeffh/intfloat-multilingual-e5-large:f16 (LM_EMBED_MODEL)
• Other envs: LM_NTOK (max tokens), LM_VISION_HF (vision repo), LM_DEBUG=1 (show llama.cpp logs)

Pass an explicit model as the first argument to ask/chat/embed/serve (e.g. lm ask qwen3:4b "...").

Critical gotchas

• Ollama's gemma3 GGUF does NOT load in stock llama.cpp. It fails with key not found in model: gemma3.attention.layer_norm_rms_epsilon because Ollama writes custom metadata keys mainline llama.cpp doesn't read. Use a qwen* model instead, or pull a community gemma3 GGUF via -hf. This is why the defaults are qwen, not gemma3.
• qwen3:4b emits <think>…</think> reasoning blocks before its answer. Fine for ask/chat, but it pollutes preset output (JSON, labels) — the presets default to qwen2.5:3b to avoid this.
• Vision has no Ollama blob to reuse. Ollama did not store an mmproj (vision projector) for qwen2.5vl, and llama.cpp needs one. So the vision commands use llama-mtmd-cli -hf ggml-org/Qwen2.5-VL-3B-Instruct-GGUF, which downloads model+projector (2–3 GB) into `/.cache/llama.cpp` on first use, then runs offline. Warn the user before the first vision call.
• Each one-shot call reloads the model (a few seconds for these small models). For many sequential calls, start a server once with lm serve and hit http://localhost:8080/v1/chat/completions — see references/serving-and-embeddings.md.

Reference material

• references/serving-and-embeddings.md — running llama-server as an OpenAI-compatible endpoint (and pointing the llm CLI or any OpenAI client at it), plus local embeddings / RAG patterns with llama-embedding.

Requirements

• llama.cpp installed (brew install llama.cpp) — provides llama-completion, llama-mtmd-cli, llama-embedding, llama-server.
• Ollama with at least one pulled model (for the blob-reuse path). python3 for the resolver. No API keys.