/llama-cpp

name: llama-cpp
description: llama.cpp local GGUF inference + HF Hub model discovery.
version: 2.1.2
author: Orchestra Research
license: MIT
dependencies: [llama-cpp-python>=0.2.0]
platforms: [linux, macos, windows]
metadata:
  hermes:
    tags: [llama.cpp, GGUF, Quantization, Hugging Face Hub, CPU Inference, Apple Silicon, Edge Deployment, AMD GPUs, Intel GPUs, NVIDIA, URL-first]

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 `num_parameters=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-Q4_K_M` or `IQ4_NL_XL`
• 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. If the local-app snippet is not text-visible, reconstruct the command from the repo plus the chosen quant:

• shorthand quant selection: `llama-server -hf <repo>:<QUANT>`
• exact-file fallback: `llama-server --hf-repo <repo> --hf-file <filename.gguf>`

7. Only suggest conversion from Transformers weights if the repo does not already expose GGUF files.

Quick start

Install llama.cpp

# macOS / Linux (simplest)
brew install llama.cpp

winget install llama.cpp

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
cmake -B build
cmake --build build --config Release

Run directly from the Hugging Face Hub

llama-cli -hf bartowski/Llama-3.2-3B-Instruct-GGUF:Q8_0

llama-server -hf bartowski/Llama-3.2-3B-Instruct-GGUF:Q8_0

Run an exact GGUF file from the Hub

Use this when the tree API shows custom file naming or the exact HF snippet is missing.

llama-server \
    --hf-repo microsoft/Phi-3-mini-4k-instruct-gguf \
    --hf-file Phi-3-mini-4k-instruct-q4.gguf \
    -c 4096

OpenAI-compatible server check

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "Write a limerick about Python exceptions"}
    ]
  }'

Python bindings (llama-cpp-python)

`pip install llama-cpp-python` (CUDA: `CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python --force-reinstall --no-cache-dir`; Metal: `CMAKE_ARGS="-DGGML_METAL=on" ...`).

Basic generation

from llama_cpp import Llama

llm = Llama(
    model_path="./model-q4_k_m.gguf",
    n_ctx=4096,
    n_gpu_layers=35,     # 0 for CPU, 99 to offload everything
    n_threads=8,
)

out = llm("What is machine learning?", max_tokens=256, temperature=0.7)
print(out["choices"][0]["text"])

Chat + streaming

llm = Llama(
    model_path="./model-q4_k_m.gguf",
    n_ctx=4096,
    n_gpu_layers=35,
    chat_format="llama-3",   # or "chatml", "mistral", etc.
)

resp = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is Python?"},
    ],
    max_tokens=256,
)
print(resp["choices"][0]["message"]["content"])

# Streaming
for chunk in llm("Explain quantum computing:", max_tokens=256, stream=True):
    print(chunk["choices"][0]["text"], end="", flush=True)

Embeddings

llm = Llama(model_path="./model-q4_k_m.gguf", embedding=True, n_gpu_layers=35)
vec = llm.embed("This is a test sentence.")
print(f"Embedding dimension: {len(vec)}")

You can also load a GGUF straight from the Hub:

llm = Llama.from_pretrained(
    repo_id="bartowski/Llama-3.2-3B-Instruct-GGUF",
    filename="*Q4_K_M.gguf",
    n_gpu_layers=35,
)

Choosing a quant

Use the Hub page first, generic heuristics second.

• Prefer the exact quant that HF marks as compatible for the user's hardware profile.
• For general chat, start with `Q4_K_M`.
• For code or technical work, prefer `Q5_K_M` or `Q6_K` if memory allows.
• For very tight RAM budgets, consider `Q3_K_M`, `IQ` variants, or `Q2` variants only if the user explicitly prioritizes fit over quality.
• For multimodal repos, mention `mmproj-*.gguf` separately. The projector is not the main model file.
• Do not normalize repo-native labels. If the page says `UD-Q4_K_M`, report `UD-Q4_K_M`.

Extracting available GGUFs from a repo

When the user asks what GGUFs exist, return:

• filename
• file size
• quant label
• whether it is a main model or an au