Text Generation
GGUF
Japanese
English
llama.cpp
Mixture of Experts
expert-pruning
intel-mac
cpu
local-agent
imatrix
conversational
Instructions to use miutti/intel-mac-local-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use miutti/intel-mac-local-llm with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf miutti/intel-mac-local-llm:UD-Q2_K_XL # Run inference directly in the terminal: llama cli -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf miutti/intel-mac-local-llm:UD-Q2_K_XL # Run inference directly in the terminal: llama cli -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf miutti/intel-mac-local-llm:UD-Q2_K_XL # Run inference directly in the terminal: ./llama-cli -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf miutti/intel-mac-local-llm:UD-Q2_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
Use Docker
docker model run hf.co/miutti/intel-mac-local-llm:UD-Q2_K_XL
- LM Studio
- Jan
- vLLM
How to use miutti/intel-mac-local-llm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "miutti/intel-mac-local-llm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "miutti/intel-mac-local-llm", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/miutti/intel-mac-local-llm:UD-Q2_K_XL
- Ollama
How to use miutti/intel-mac-local-llm with Ollama:
ollama run hf.co/miutti/intel-mac-local-llm:UD-Q2_K_XL
- Unsloth Desktop
- Pi
How to use miutti/intel-mac-local-llm with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "miutti/intel-mac-local-llm:UD-Q2_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use miutti/intel-mac-local-llm with Docker Model Runner:
docker model run hf.co/miutti/intel-mac-local-llm:UD-Q2_K_XL
- Lemonade
How to use miutti/intel-mac-local-llm with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull miutti/intel-mac-local-llm:UD-Q2_K_XL
Run and chat with the model
lemonade run user.intel-mac-local-llm-UD-Q2_K_XL
List all available models
lemonade list
- Hermes Agent
How to use miutti/intel-mac-local-llm with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default miutti/intel-mac-local-llm:UD-Q2_K_XL
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use miutti/intel-mac-local-llm with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf miutti/intel-mac-local-llm:UD-Q2_K_XL
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "miutti/intel-mac-local-llm:UD-Q2_K_XL" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download source/kernel/audio.py from miutti/intel-mac-local-llm: direct link, hf CLI and curl.
- Browser
- Download file 4.38 kB
-
https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/audio.py
- Command line
-
hf download hf://miutti/intel-mac-local-llm/source/kernel/audio.py
-
curl -L -o audio.py https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/audio.py
4.38 kB
| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| """ | |
| audio.py -- 音のようすを日本語で言う | |
| 「何を言ったか」ではなく「どんな音か」。 | |
| 強弱 … 大きさ(デシベル) | |
| 高さ … 声や音の高さ(Hz) | |
| 音色 … 明るいか、こもっているか(スペクトル重心) | |
| ざらつき… 息や雑音の多さ(ゼロ交差率) | |
| 区切り … 鳴っているところ/静かなところ | |
| 【実測】同じ文を、別の声でしゃべらせたとき | |
| Kyoko 256.4 Hz 明るさ 2188 (高くて明るい) | |
| Eddy 107.0 Hz 明るさ 1362 | |
| Grandma 98.9 Hz 明るさ 1138 | |
| Grandpa 84.8 Hz 明るさ 723 (低くてこもっている) | |
| ちゃんと別々の数字になる。 | |
| 使うのは macOS の AVFoundation と Accelerate だけ。ネットには出ない。 | |
| """ | |
| import json, os, subprocess | |
| HERE = os.path.dirname(os.path.abspath(__file__)) | |
| HEAR = os.path.join(HERE, "tools", "hear") | |
| EXT = {".aiff", ".aif", ".wav", ".m4a", ".mp3", ".caf", ".aac", ".flac"} | |
| def ready(): | |
| return os.path.exists(HEAR) | |
| def hear(path, timeout=120): | |
| if not ready(): | |
| raise Exception("音を聞く道具がありません(tools/hear)") | |
| path = os.path.expanduser(path) | |
| if not os.path.exists(path): | |
| raise Exception(f"その音のファイルがありません: {path}") | |
| r = subprocess.run([HEAR, path], capture_output=True, text=True, | |
| timeout=timeout) | |
| if r.returncode != 0: | |
| raise Exception((r.stderr or "聞けませんでした").strip()[:200]) | |
| return json.loads(r.stdout) | |
| # ---- 数字を、ことばに直す ---- | |
| def _loud(db): | |
| if db > -12: return "とても大きい" | |
| if db > -20: return "大きい" | |
| if db > -30: return "ふつう" | |
| if db > -45: return "小さい" | |
| return "とても小さい" | |
| def _pitch(hz): | |
| if hz <= 0: return "高さがはっきりしない(音楽でない音や、雑音)" | |
| if hz < 100: return f"とても低い({hz:.0f} Hz) 男性の低い声くらい" | |
| if hz < 160: return f"低い({hz:.0f} Hz) 男性の声くらい" | |
| if hz < 260: return f"中くらい({hz:.0f} Hz) 女性の声くらい" | |
| if hz < 400: return f"高い({hz:.0f} Hz)" | |
| return f"とても高い({hz:.0f} Hz)" | |
| def _tone(c): | |
| if c <= 0: return "音色が測れない" | |
| if c < 900: return f"こもった、丸い音(重心 {c:.0f} Hz)" | |
| if c < 1800: return f"落ちついた音(重心 {c:.0f} Hz)" | |
| if c < 3200: return f"明るい音(重心 {c:.0f} Hz)" | |
| return f"とても明るい・鋭い音(重心 {c:.0f} Hz)" | |
| def _rough(z): | |
| if z < 0.05: return "なめらか(きれいな音)" | |
| if z < 0.12: return "すこしざらつく" | |
| if z < 0.25: return "ざらつきが多い(息や雑音まじり)" | |
| return "とてもざらつく(雑音が主)" | |
| def describe(path): | |
| d = hear(path) | |
| g = d["全体"] | |
| segs = d["鳴っているところ"] | |
| rate = len(segs) / d["長さ秒"] if d["長さ秒"] else 0 | |
| out = [f"{os.path.basename(path)}" | |
| f"({d['長さ秒']} 秒 / {d['サンプリング周波数']:,} Hz / " | |
| f"{d['音の道']} つの音の道)", | |
| f" 強弱 : {_loud(g['デシベル'])}" | |
| f"({g['デシベル']} dB、山は {g['いちばん大きいところ']} dB)", | |
| f" 高さ : {_pitch(g['高さ中央値'])}", | |
| f" 音色 : {_tone(g['明るさ中央値'])}", | |
| f" ざらつき: {_rough(g['ざらつき'])}({g['ざらつき']})", | |
| f" 鳴っているところ: {len(segs)} か所"] | |
| for s in segs[:8]: | |
| out.append(f" {s['始まり']:.2f} 〜 {s['おわり']:.2f} 秒") | |
| if len(segs) > 8: | |
| out.append(f" …ほか {len(segs)-8} か所") | |
| if rate > 1.2: | |
| out.append(" → 切れ目が多いので、話し声か、たたく音かもしれません") | |
| elif len(segs) == 1 and d["長さ秒"] < 3: | |
| out.append(" → ひと続きの短い音。合図の音などでしょう") | |
| return "\n".join(out) | |
| if __name__ == "__main__": | |
| import sys | |
| for p in sys.argv[1:] or []: | |
| print(describe(p)); print() | |
| if not sys.argv[1:]: | |
| print(" 使い方: python3 audio.py <音のファイル>") | |