Agent skill · nousresearch
llama-cpp
llama.cpp local GGUF inference + HF Hub model discovery.
What it needs
About 4k tokens when loaded.
What this skill does
llama.cpp + GGUF Use this skill for local GGUF inference, quant selection, or Hugging Face repo discovery for llama.cpp. When to use Run local models on CPU, Apple Silicon, CUDA, ROCm, or Intel GPUs Find the right GGUF for a specific Hugging Face repo Build a llama-server or llama-cli command from the Hub Search the Hub for models that already support llama.cpp Enumerate available .gguf files and sizes for a repo Decide between Q4/Q5/Q6/IQ variants for the user's RAM or VRAM Model Discovery workflow Prefer URL workflows before asking for hf, Python, or custom scripts. 1. Search for candidate repos on the Hub: Base: https://huggingface.co/models?apps=llama.cpp&sort=trending Add search=<term> for a model family Add numparameters=min:0,max:24B or similar when the user has size constraints 2. Open the repo with the llama.cpp local-app view: https://huggingface.co/<repo>?local-app=llama.cpp 3. Treat the local-app snippet as the source of truth when it is visible: copy the exact llama-server or llama-cli command report the recommended quant exactly as HF shows it 4. Read the same ?local-app=llama.cpp URL as page text or HTML and extract the section under Hardware compatibility: prefer its exact quant labels and sizes over generic tables keep repo-specific labels such as UD-Q4KM or IQ4NLXL if that section is not visible in the fetched page source, say so and fall back to the tree API plus generic quant guidance 5. Query the tree API to confirm what actually exists: https://huggingface.co/api/models/<repo>/tree/main?recursive=true keep entries where type is file and path ends with .gguf use path and size as the source of truth for filenames and byte sizes separate quantized checkpoints from mmproj-.gguf projector files and BF16/ shard files use https://huggingface.co/<repo>/tree/main only as a human fallback 6. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nousresearch/llama-cpp--124f2f