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/dict_build.py from miutti/intel-mac-local-llm: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/dict_build.py
- Command line
-
hf download hf://miutti/intel-mac-local-llm/source/kernel/dict_build.py
-
curl -L -o dict_build.py https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/dict_build.py
4.03 kB
| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| """ | |
| dict_build.py -- 日本語Wiktionaryのダンプから、語と説明の対を取り出す | |
| ネットを叩き続けるのは失礼だし遅い(実際429で止められた)。 | |
| Wikimedia 自身が「大量に欲しければダンプを使え」と言っている。 | |
| 一度落とせば、あとは手元で何度でも辿れる。 | |
| 出力: dict/pages.jsonl (1行1語。{"語":..., "説明":...}) | |
| """ | |
| import os, sys, json, bz2, re, time, xml.etree.ElementTree as ET | |
| HERE = os.path.dirname(os.path.abspath(__file__)) | |
| DUMP = os.path.join(HERE, "dict", "jawiktionary.xml.bz2") | |
| OUT = os.path.join(HERE, "dict", "pages.jsonl") | |
| NS = "{http://www.mediawiki.org/xml/export-0.11/}" | |
| JP = re.compile(r"[ぁ-んァ-ヴー一-龥]") | |
| SKIP = re.compile(r"^(Wiktionary|カテゴリ|Category|テンプレート|Template|" | |
| r"MediaWiki|ヘルプ|Help|付録|索引|モジュール|Module|ファイル|File|" | |
| r"利用者|User|トーク|Talk|ノート):") | |
| # 中身が運営の話になっているページ。語の意味とは無関係なので捨てる | |
| ADMIN = re.compile(r"(削除依頼|このページは|進行中の依頼|保護依頼|移動依頼|" | |
| r"荒らし|投稿ブロック|管理者|署名|ウィキ(?:ペディア|辞典)は)") | |
| # ウィキ記法を落として、素の文にする | |
| CLEAN = [ | |
| (re.compile(r"\{\{[^{}]*\}\}"), " "), # テンプレート | |
| (re.compile(r"\[\[(?:[^\]|]*\|)?([^\]]*)\]\]"), r"\1"), # 内部リンク | |
| (re.compile(r"<ref[^>]*>.*?</ref>", re.S), " "), | |
| (re.compile(r"<[^>]+>"), " "), | |
| (re.compile(r"'''?"), ""), | |
| (re.compile(r"^[=*#:;|!]+", re.M), " "), | |
| (re.compile(r"\[https?://\S+\s*([^\]]*)\]"), r"\1"), | |
| (re.compile(r"https?://\S+"), " "), | |
| (re.compile(r"[ \t]{2,}"), " "), | |
| # 画像・分類・言語見出しなど、文になっていない行の材料 | |
| (re.compile(r"(?:thumb|right|left|center|\d+px)\|"), " "), | |
| (re.compile(r"(?:Category|カテゴリ|File|ファイル|Image|画像):[^\n]*"), " "), | |
| (re.compile(r"^\s*(?:\{\||\|\}|\|-|\|).*$", re.M), " "), | |
| ] | |
| def clean(t): | |
| for p, r in CLEAN: | |
| t = p.sub(r, t) | |
| lines = [] | |
| for l in t.splitlines(): | |
| l = l.strip(" \t|=*#:;") | |
| if len(l) < 4 or not JP.search(l): | |
| continue | |
| # かなが1文字も無い行は、表や分類の残りかすが多い | |
| if not re.search(r"[ぁ-んァ-ヴ]", l) and len(l) < 12: | |
| continue | |
| lines.append(l) | |
| return "\n".join(lines) | |
| def run(limit=None, report=20000): | |
| t0, n, kept = time.time(), 0, 0 | |
| fout = open(OUT, "w", encoding="utf-8") | |
| with bz2.open(DUMP, "rb") as f: | |
| title = None | |
| for ev, el in ET.iterparse(f, events=("end",)): | |
| tag = el.tag[len(NS):] if el.tag.startswith(NS) else el.tag | |
| if tag == "title": | |
| title = el.text | |
| elif tag == "page": | |
| n += 1 | |
| txt = None | |
| for t in el.iter(NS + "text"): | |
| txt = t.text | |
| if title and txt and not SKIP.match(title): | |
| body = clean(txt) | |
| if (len(body) >= 20 and JP.search(title) | |
| and not ADMIN.search(body[:600])): | |
| fout.write(json.dumps({"語": title, "説明": body[:6000]}, | |
| ensure_ascii=False) + "\n") | |
| kept += 1 | |
| el.clear() | |
| if n % report == 0: | |
| el_s = time.time() - t0 | |
| print(f" 読んだ {n} / 使えた {kept} ({el_s:.0f}秒)", flush=True) | |
| if limit and kept >= limit: | |
| break | |
| fout.close() | |
| print(f" → {OUT} {kept} 語 {os.path.getsize(OUT)/1e6:.0f} MB " | |
| f"({time.time()-t0:.0f}秒)") | |
| if __name__ == "__main__": | |
| run(limit=int(sys.argv[1]) if len(sys.argv) > 1 else None) | |