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"
File size: 4,377 Bytes
df41178 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | #!/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>")
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