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: 9,568 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
background.py -- 裏でずっと辞書を辿り続ける係
やること:
1. 調べた語のまわりを、辞書を辿って広げていく(「かな文字って何?」の連鎖)
2. 辿った結果を bg_web.json に貯める。次からの「関連」が速くなる
3. 札になりそうな語を見つけたら、候補として置いておく
【大事な決めごと】
候補は、勝手に札にしない。
辞書ぜんぶを一気に札にしたら、421枚のうち半分以上が
「宇宙飛行士 → 画像」「醤油 → PDF」のような間違いだった。
だから裏の係は「これはどうですか」と置くところまで。
採るかどうかは本人が /candidates で決める。
動かし方:
/bg start はじめる(裏で動き続ける)
/bg stop とめる
/bg status いまどこまで進んだか
"""
import os, sys, json, time, signal
HERE = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, HERE)
STATE = os.path.join(HERE, "bg_state.json")
QUEUE = os.path.join(HERE, "bg_queue.json")
WEB = os.path.join(HERE, "bg_web.json")
CAND = os.path.join(HERE, "card_candidates.json")
STOP = os.path.join(HERE, "bg_stop")
LOG = os.path.join(HERE, "bg.log")
# 1語ごとに少し休む。裏の係が前を邪魔しないように
REST = 0.03
# 貯めすぎない上限
MAX_WEB, MAX_CAND = 40000, 2000
def _read(path, default):
try:
with open(path, encoding="utf-8") as f:
return json.load(f)
except Exception:
return default
def _write(path, obj):
tmp = path + ".tmp"
with open(tmp, "w", encoding="utf-8") as f:
json.dump(obj, f, ensure_ascii=False)
os.replace(tmp, path)
def status():
st = _read(STATE, {})
if not st:
return "裏の係は、まだ一度も動いていません。"
alive = False
pid = st.get("pid")
if pid:
try:
os.kill(pid, 0)
alive = True
except OSError:
alive = False
age = time.time() - st.get("更新", 0)
web = _read(WEB, {})
cand = _read(CAND, {})
q = _read(QUEUE, [])
if alive and age > 120:
head = (f"生きてはいるが、{age/60:.0f} 分ものあいだ合図がありません。"
f"引っかかっている可能性があります(/bg stop で止められます)")
elif alive:
head = f"動いています(最後の合図は {age:.0f} 秒前)"
elif st.get("終わった"):
head = f"とまっています({st['終わった']}・{age/60:.0f} 分前)"
else:
head = "とまっています(理由は分かりません。落ちた可能性があります)"
lines = [
f" うごき : {head}",
f" 調べた語 : {st.get('調べた', 0):,} 語",
f" 待っている語: {len(q):,} 語",
f" つながり : {len(web):,} 語ぶん(bg_web.json)",
f" 札の候補 : {len(cand)} 件 ← /candidates で見られます",
]
if st.get("いま"):
lines.append(f" いま : 「{st['いま']}」")
return "\n".join(lines)
def seed_from(words):
"""調べたい語を、待ち行列の先頭に足す"""
q = _read(QUEUE, [])
have = set(q)
add = [w for w in words if w and w not in have]
_write(QUEUE, add + q)
return len(add)
def stop():
open(STOP, "w").close()
st = _read(STATE, {})
pid = st.get("pid")
if pid:
try:
os.kill(pid, signal.SIGTERM)
except OSError:
pass
return "とめました。"
def start():
"""裏の係を、別のプロセスとして立ち上げる"""
st = _read(STATE, {})
pid = st.get("pid")
if pid:
try:
os.kill(pid, 0)
return "もう動いています。(/bg status で様子が見られます)"
except OSError:
pass
if os.path.exists(STOP):
os.remove(STOP)
import subprocess
with open(LOG, "a") as lg:
p = subprocess.Popen(
["nice", "-n", "10", sys.executable, __file__, "--run"],
stdout=lg, stderr=lg, stdin=subprocess.DEVNULL,
start_new_session=True, cwd=HERE)
time.sleep(0.6)
return f"裏で動きはじめました(pid {p.pid})。/bg status で様子が見られます。"
# ============================================================
# ここからが、裏で回り続ける本体
# ============================================================
def run():
import lookup, kernel, grow
d = lookup.Dict()
kernel.load_learned()
base = {k: v for k, v in kernel.SEED.items()
if v[0] in ("種類", "場所") and len(k) >= 2}
all_words = list(d.idx) # 辞書に載っている語ぜんぶ
q = _read(QUEUE, [])
if not q:
# 何も指定が無ければ、種火の語そのものから辿りはじめる
q = [k for k in kernel.SEED if len(k) >= 2]
web = _read(WEB, {})
cand = _read(CAND, {})
done = set(web)
n = _read(STATE, {}).get("調べた", 0)
skipped = 0
def save(now=""):
_write(STATE, {"pid": os.getpid(), "更新": time.time(),
"調べた": n, "いま": now}) # 「終わった」は消える
_write(QUEUE, q[:20000])
_write(WEB, web)
_write(CAND, cand)
save()
last = time.time()
while True:
if os.path.exists(STOP):
break
if not q:
# 辿る先を使い切ったら、辞書のまだ見ていない語から補充する。
# ここが無いと 4,804 語で自然に止まってしまい、
# 「ずっと動き続ける」はずが静かに終わっていた
fresh = [w for w in all_words if w not in done][:5000]
print(f"[{time.strftime('%H:%M:%S')}] 補充 {len(fresh)} 語"
f"(見た {len(done):,} / 見送り {skipped:,})")
if not fresh:
_write(STATE, {"更新": time.time(), "調べた": n,
"いま": "", "終わった": "辞書を全部見ました"})
break
q.extend(fresh)
print(f"[{time.strftime('%H:%M:%S')}] 辞書から {len(fresh)} 語 補充")
w = q.pop(0)
# 見送る語も「見た」に入れる。入れていなかったので、
# 短すぎる語・長すぎる語が補充のたびに何度でも戻ってきて、
# 1時間ずっとCPUを100%使って空回りしていた
if w in done or not (2 <= len(w) <= 20):
done.add(w)
skipped += 1
if skipped % 500 == 0:
save(w) # 見送り続きでも、生きている合図は出す
time.sleep(REST) # 休みを挟む。全力で回らせない
continue
done.add(w)
try:
body = d.look(w)
rel = d.links(w, 8) if body else []
except Exception:
body, rel = None, []
n += 1
if body:
if len(web) < MAX_WEB:
web[w] = rel[:8]
# まだ見ていない語を、後ろに足す(これが「永遠に辿る」の中身)
for r in rel[:6]:
if r not in done and len(q) < MAX_WEB:
q.append(r)
# 札になりそうか見るだけ。採用はしない
if len(cand) < MAX_CAND and w not in kernel.SEED and w not in grow.load():
first = next((l.strip() for l in body.split("\n") if l.strip()), "")
r = grow.from_definition(w, first, base)
if r and len(first) <= 40:
slot, val, why = r
# ── 種類の札は、pc_kind に一度 相談してから ──
# 説明文に「資料」の2字が入っているだけで
# 醤油 → PDF(根拠「資料」)
# メモリ → テキスト(根拠「メモ」)
# のような札が候補に並んでいた。字面の一致でしかない。
# pc_kind は記事の出方で見るので、こういう語には黙る。
# 黙られた語は候補にしない。
if slot == "種類":
try:
import pc_kind
if pc_kind.ready():
g = pc_kind.guess(w)
if not g or g[0] is None or g[0] != val:
skipped += 1
continue
except Exception:
pass
cand[w] = {"枠": slot, "値": val, "根拠": why,
"説明": first[:60]}
if time.time() - last > 5:
save(w)
last = time.time()
time.sleep(REST)
save()
st = _read(STATE, {})
st.pop("pid", None)
st.setdefault("終わった", "とめられました")
st["更新"] = time.time()
_write(STATE, st)
print(f"[{time.strftime('%H:%M:%S')}] おわり: {n} 語({st['終わった']})")
if __name__ == "__main__":
if "--run" in sys.argv:
run()
elif "--status" in sys.argv:
print(status())
else:
print(__doc__)
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