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
- Llama new
- 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: 6,501 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 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
conjugate.py -- 動詞の活用を、規則で作る
「移す」から「移して」「移した」「移しといて」…を、
ひとつずつ書き並べるのではなく、国語で習う活用表から自動で作る。
なぜ規則にしたか
────────────────
はじめは外のAIに「述語を225個ならべて」と頼んだ。
返ってきたのは52個で、しかも「寄せられるように」のような
命令にならない形が混ざっていた。
日本語の活用は規則で決まっている。
規則で作れば、抜けもなく、間違いもなく、ただでいくらでも作れる。
五段・一段・サ変の3つだけ扱う。頼みごとに使う動詞は、ほぼこれで足りる。
五段活用の音の変化(学校で習う「行段」の表):
書く → 書か 書き 書く 書け 書こ / て形は 書いて
移す → 移さ 移し 移す 移せ 移そ / て形は 移して
運ぶ → 運ば 運び 運ぶ 運べ 運ぼ / て形は 運んで
"""
# 五段活用。語尾の1文字 → (連用形の音, て形, た形)
GODAN = {
"う": ("い", "って", "った"),
"つ": ("ち", "って", "った"),
"る": ("り", "って", "った"),
"む": ("み", "んで", "んだ"),
"ぶ": ("び", "んで", "んだ"),
"ぬ": ("に", "んで", "んだ"),
"く": ("き", "いて", "いた"),
"ぐ": ("ぎ", "いで", "いだ"),
"す": ("し", "して", "した"),
}
# 五段の未然形(〜ない/〜せる の前)
GODAN_A = {"う": "わ", "つ": "た", "る": "ら", "む": "ま", "ぶ": "ば",
"ぬ": "な", "く": "か", "ぐ": "が", "す": "さ"}
# 五段の命令形
GODAN_E = {"う": "え", "つ": "て", "る": "れ", "む": "め", "ぶ": "べ",
"ぬ": "ね", "く": "け", "ぐ": "げ", "す": "せ"}
# 「て」の後に付く、頼みごとの言い方
TE_TAILS = [
"", "ください", "くれ", "くれる", "ほしい", "ほしいな", "もらえる",
"もらえます", "おいて", "おいてね", "ね", "よ", "な",
]
# 連用形の後に付く言い方
RENYOU_TAILS = ["たい", "ます", "ましょう", "なさい"]
def _split(verb):
"""動詞を (語幹, 種類) に分ける。
種類は "五段" / "一段" / "サ変" / None
"""
if verb.endswith("する"):
return verb[:-2], "サ変"
if len(verb) >= 2 and verb[-1] == "る":
# 「見る・食べる」のような一段は、直前が「い段・え段」
prev = verb[-2]
if _is_i_or_e(prev):
return verb[:-1], "一段"
return verb[:-1], "五段"
if verb and verb[-1] in GODAN:
return verb[:-1], "五段"
return verb, None
_I_ROW = set("いきしちにひみりぎじびぴ")
_E_ROW = set("えけせてねへめれげぜべぺ")
def _is_i_or_e(ch):
return ch in _I_ROW or ch in _E_ROW
def forms(verb):
"""ひとつの動詞から、頼みごとに使われる言い方を全部作る"""
stem, kind = _split(verb)
out = set()
if kind == "サ変":
te, ta, ren = stem + "して", stem + "した", stem + "し"
base = [verb, stem + "しろ", stem + "せよ", stem + "させて",
stem + "させといて", stem + "しといて"]
elif kind == "一段":
te, ta, ren = stem + "て", stem + "た", stem
base = [verb, stem + "ろ", stem + "よ", stem + "させて",
stem + "させといて", stem + "といて"]
elif kind == "五段":
last = verb[-1]
ren = stem + GODAN[last][0]
te = stem + GODAN[last][1]
ta = stem + GODAN[last][2]
base = [verb, stem + GODAN_E[last],
stem + GODAN_A[last] + "せて",
stem + GODAN_A[last] + "せといて",
te[:-1] + "といて" if te.endswith("て") else te]
else:
return {verb}
out.update(b for b in base if b)
for t in TE_TAILS:
out.add(te + t)
for t in RENYOU_TAILS:
out.add(ren + t)
# 「〜とく」の縮約(移しておく → 移しとく)
out.add(te[:-1] + "とく" if te.endswith("て") else te)
return {w for w in out if 2 <= len(w) <= 12}
# ------------------------------------------------------------------
# 動作ごとの、もとになる動詞
# ここだけ人が選ぶ。あとの活用は機械が作る
# ------------------------------------------------------------------
BASE_VERBS = {
"移動": ["移す", "移動する", "動かす", "まとめる", "集める", "寄せる",
"入れる", "しまう", "片付ける", "整理する", "放り込む",
"分ける", "仕分ける", "送る", "持っていく"],
"数える": ["数える", "カウントする", "調べる"],
"一覧": ["見せる", "並べる", "listする", "出す", "教える", "表示する"],
"作成": ["作る", "作成する", "用意する", "新規作成する", "こしらえる"],
"ごみばこ": ["捨てる", "消す", "削除する", "始末する", "処分する",
"ゴミ箱に入れる"],
"改名": ["改名する", "リネームする", "名前を変える", "つけ直す"],
"重複": ["重複する", "かぶる", "だぶる"],
"大きさ": ["合計する"],
"もぐる": ["もぐる", "潜る"],
"HTML": ["HTML化する", "ページにする"],
}
def build():
"""全部の動作について、述語カードを作る。
戻り値: {述語: 動作}
"""
out = {}
for act, verbs in BASE_VERBS.items():
for v in verbs:
for f in forms(v):
# 先に入った動作を優先する(早い者勝ちで衝突を避ける)
out.setdefault(f, act)
return out
if __name__ == "__main__":
import sys, json
if len(sys.argv) > 1:
for v in sys.argv[1:]:
print(f"\n{v} → " + " / ".join(sorted(forms(v))))
else:
t = build()
from collections import Counter
c = Counter(t.values())
print(f"できた述語: {len(t)} 個")
for act, n in c.most_common():
print(f" {act:<8} {n:>4} 個")
print("\n見本(移動):")
print(" " + " / ".join(sorted(k for k, v in t.items()
if v == "移動")[:24]))
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