Agent skill · AI & Agents

huggingface-local-models

Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.

Wayner Barrios443★ · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add waybarrios/opencode-power-pack --skill huggingface-local-models --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 4
SKILL.md size: 4 KB
Bundled scripts: none
Path: skills/huggingface-local-models/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 443
Language: JavaScript
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Hugging Face Local Models Search the Hugging Face Hub for llama.cpp-compatible GGUF repos, choose the right quant, and launch the model with `llama-cli` or `llama-server`. ## Default Workflow 1. Search the Hub with `apps=llama.cpp`. 2. Open `https://huggingface.co/<repo>?local-app=llama.cpp`. 3. Prefer the exact HF local-app snippet and quant recommendation when it is visible. 4. Confirm exact `.gguf` filenames with `https://huggingface.co/api/models/<repo>/tree/main?recursive=true`. 5. Launch with `llama-cli -hf <repo>:<QUANT>` or `llama-server -hf <repo>:<QUANT>`. 6. Fall back to `--hf-repo` plus `--hf-file` when the repo uses custom file naming. 7. Convert from Transformers weights only if the repo does not already expose GGUF files. ## Quick Start ### Install llama.cpp ```bash brew install llama.cpp winget install llama.cpp ``` ```bash git clone https://github.com/ggml-org/llama.cpp cd llama.cpp make ``` ### Authenticate for gated repos ```bash hf auth login ``` ### Search the Hub ```text https://huggingface.co/models?apps=llama.cpp&sort=trending https://huggingface.co/models?search=Qwen3.6&apps=llama.cpp&sort=trending https://huggingface.co/models?search=<term>&apps=llama.cp

What's inside
Steps it walks through
  1. Default Workflow
  2. Quick Start
  3. Install llama.cpp
  4. Authenticate for gated repos
  5. Search the Hub
  6. Run directly from the Hub
  7. Run an exact GGUF file
  8. Convert only when no GGUF is available
  9. Smoke test a local server
  10. Quant Choice
  11. Load References
  12. Resources
Ships with 3 files
  • references/hardware.md
  • references/hub-discovery.md
  • references/quantization.md
Commands it runs
brew install llama.cpp
winget install llama.cpp
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
make
hf auth login
llama-cli -hf unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M
llama-server -hf unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M
llama-server \
hf download <repo-without-gguf> --local-dir ./model-src
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About this skill
What does the huggingface-local-models skill do?

Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.

How do I install it?

Run `npx skills add waybarrios/opencode-power-pack --skill huggingface-local-models --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From waybarrios/opencode-power-pack, a repository with 443 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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