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docs(website): dedicated page per bundled + optional skill (#14929)

Browse source 
Generates a full dedicated Docusaurus page for every one of the 132 skills
(73 bundled + 59 optional) under website/docs/user-guide/skills/{bundled,optional}/<category>/.
Each page carries the skill's description, metadata (version, author, license,
dependencies, platform gating, tags, related skills cross-linked to their own
pages), and the complete SKILL.md body that Hermes loads at runtime.

Previously the two catalog pages just listed skills with a one-line blurb and
no way to see what the skill actually did — users had to go read the source
repo. Now every skill has a browsable, searchable, cross-linked reference in
the docs.

- website/scripts/generate-skill-docs.py — generator that reads skills/ and
  optional-skills/, writes per-skill pages, regenerates both catalog indexes,
  and rewrites the Skills section of sidebars.ts. Handles MDX escaping
  (outside fenced code blocks: curly braces, unsafe HTML-ish tags) and
  rewrites relative references/*.md links to point at the GitHub source.
- website/docs/reference/skills-catalog.md — regenerated; each row links to
  the new dedicated page.
- website/docs/reference/optional-skills-catalog.md — same.
- website/sidebars.ts — Skills section now has Bundled / Optional subtrees
  with one nested category per skill folder.
- .github/workflows/{docs-site-checks,deploy-site}.yml — run the generator
  before docusaurus build so CI stays in sync with the source SKILL.md files.

Build verified locally with `npx docusaurus build`. Only remaining warnings
are pre-existing broken link/anchor issues in unrelated pages.
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---
title: "Llama Cpp — llama"
sidebar_label: "Llama Cpp"
description: "llama"
---
{/* This page is auto-generated from the skill's SKILL.md by website/scripts/generate-skill-docs.py. Edit the source SKILL.md, not this page. */}
# Llama Cpp
llama.cpp local GGUF inference + HF Hub model discovery.
## Skill metadata
| | |
|---|---|
| Source | Bundled (installed by default) |
| Path | `skills/mlops/inference/llama-cpp` |
| Version | `2.1.2` |
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `llama-cpp-python>=0.2.0` |
| Tags | `llama.cpp`, `GGUF`, `Quantization`, `Hugging Face Hub`, `CPU Inference`, `Apple Silicon`, `Edge Deployment`, `AMD GPUs`, `Intel GPUs`, `NVIDIA`, `URL-first` |
## Reference: full SKILL.md
:::info
The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.
:::
# 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
```bash
# macOS / Linux (simplest)
brew install llama.cpp
```
```bash
winget install llama.cpp
```
```bash
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
```bash
llama-cli -hf bartowski/Llama-3.2-3B-Instruct-GGUF:Q8_0
```
```bash
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.
```bash
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
```bash
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
```python
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
```python
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
```python
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:
```python
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 auxiliary projector
Ignore unless requested:
- README
- BF16 shard files
- imatrix blobs or calibration artifacts
Use the tree API for this step:
- `https://huggingface.co/api/models/<repo>/tree/main?recursive=true`
For a repo like `unsloth/Qwen3.6-35B-A3B-GGUF`, the local-app page can show quant chips such as `UD-Q4_K_M`, `UD-Q5_K_M`, `UD-Q6_K`, and `Q8_0`, while the tree API exposes exact file paths such as `Qwen3.6-35B-A3B-UD-Q4_K_M.gguf` and `Qwen3.6-35B-A3B-Q8_0.gguf` with byte sizes. Use the tree API to turn a quant label into an exact filename.
## Search patterns
Use these URL shapes directly:
```text
https://huggingface.co/models?apps=llama.cpp&sort=trending
https://huggingface.co/models?search=<term>&apps=llama.cpp&sort=trending
https://huggingface.co/models?search=<term>&apps=llama.cpp&num_parameters=min:0,max:24B&sort=trending
https://huggingface.co/<repo>?local-app=llama.cpp
https://huggingface.co/api/models/<repo>/tree/main?recursive=true
https://huggingface.co/<repo>/tree/main
```
## Output format
When answering discovery requests, prefer a compact structured result like:
```text
Repo: <repo>
Recommended quant from HF: <label> (<size>)
llama-server: <command>
Other GGUFs:
- <filename> - <size>
- <filename> - <size>
Source URLs:
- <local-app URL>
- <tree API URL>
```
## References
- **[hub-discovery.md](https://github.com/NousResearch/hermes-agent/blob/main/skills/mlops/inference/llama-cpp/references/hub-discovery.md)** - URL-only Hugging Face workflows, search patterns, GGUF extraction, and command reconstruction
- **[advanced-usage.md](https://github.com/NousResearch/hermes-agent/blob/main/skills/mlops/inference/llama-cpp/references/advanced-usage.md)** — speculative decoding, batched inference, grammar-constrained generation, LoRA, multi-GPU, custom builds, benchmark scripts
- **[quantization.md](https://github.com/NousResearch/hermes-agent/blob/main/skills/mlops/inference/llama-cpp/references/quantization.md)** — quant quality tradeoffs, when to use Q4/Q5/Q6/IQ, model size scaling, imatrix
- **[server.md](https://github.com/NousResearch/hermes-agent/blob/main/skills/mlops/inference/llama-cpp/references/server.md)** — direct-from-Hub server launch, OpenAI API endpoints, Docker deployment, NGINX load balancing, monitoring
- **[optimization.md](https://github.com/NousResearch/hermes-agent/blob/main/skills/mlops/inference/llama-cpp/references/optimization.md)** — CPU threading, BLAS, GPU offload heuristics, batch tuning, benchmarks
- **[troubleshooting.md](https://github.com/NousResearch/hermes-agent/blob/main/skills/mlops/inference/llama-cpp/references/troubleshooting.md)** — install/convert/quantize/inference/server issues, Apple Silicon, debugging
## Resources
- **GitHub**: https://github.com/ggml-org/llama.cpp
- **Hugging Face GGUF + llama.cpp docs**: https://huggingface.co/docs/hub/gguf-llamacpp
- **Hugging Face Local Apps docs**: https://huggingface.co/docs/hub/main/local-apps
- **Hugging Face Local Agents docs**: https://huggingface.co/docs/hub/agents-local
- **Example local-app page**: https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF?local-app=llama.cpp
- **Example tree API**: https://huggingface.co/api/models/unsloth/Qwen3.6-35B-A3B-GGUF/tree/main?recursive=true
- **Example llama.cpp search**: https://huggingface.co/models?num_parameters=min:0,max:24B&apps=llama.cpp&sort=trending
- **License**: MIT