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/conjugate.py from miutti/intel-mac-local-llm: direct link, hf CLI and curl.
- Browser
- Download file 6.5 kB
-
https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/conjugate.py
- Command line
-
hf download hf://miutti/intel-mac-local-llm/source/kernel/conjugate.py
-
curl -L -o conjugate.py https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/conjugate.py
6.5 kB
| #!/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])) | |