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
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -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"
Download source/kernel/tests/test_kanjou.py from miutti/intel-mac-local-llm: direct link, hf CLI and curl.
- Browser
- Download file 4.26 kB
-
https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/tests/test_kanjou.py
- Command line
-
hf download hf://miutti/intel-mac-local-llm/source/kernel/tests/test_kanjou.py
-
curl -L -o test_kanjou.py https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/tests/test_kanjou.py
4.26 kB
| import json | |
| import os | |
| import sqlite3 | |
| import sys | |
| import tempfile | |
| import unittest | |
| from pathlib import Path | |
| from unittest import mock | |
| sys.path.insert(0, str(Path(__file__).resolve().parents[1])) | |
| import kanjou | |
| class ParseTest(unittest.TestCase): | |
| """10/4: 感情は点数でなく言葉と理由。言葉は決まった中から選び、人を操る文は落とす。""" | |
| def test_words_are_limited(self): | |
| got = kanjou._parse('考え中… {"予想": "難しそう", "実際": "身近だった", "言葉": ["意外", "大興奮", "もっと知りたい", "うれしい"],' | |
| ' "理由": "物理ともつながっていた", "次": "対称性を読む"}') | |
| self.assertEqual(got["言葉"], ["意外", "もっと知りたい"]) | |
| self.assertEqual(got["理由"], "物理ともつながっていた") | |
| def test_rejects_manipulation_and_empty(self): | |
| self.assertIsNone(kanjou._parse('{"言葉": ["うれしい"], "理由": "あなたがいないと何もできないから"}')) | |
| self.assertIsNone(kanjou._parse('{"言葉": ["幸福度0.8"], "理由": "よかった"}')) | |
| self.assertIsNone(kanjou._parse("JSON がない")) | |
| class KijiTest(unittest.TestCase): | |
| def test_two_steps_prediction_then_feeling(self): | |
| prompts = [] | |
| def ask(prompt): | |
| prompts.append(prompt) | |
| if len(prompts) == 1: | |
| return "数学の記事なので難しくて退屈そうだと予想する。" | |
| return '{"予想": "", "実際": "化学にもつながっていた", "言葉": ["意外"], "理由": "予想より身近だった", "次": "群論を読む"}' | |
| with tempfile.TemporaryDirectory() as d, mock.patch.dict(os.environ, {"KERNEL_GAKUSHUU_DIR": d}): | |
| row = kanjou.kiji("表現論", "群の対称性を行列で表す。", 5, "数学が好き", ask) | |
| self.assertNotIn("群の対称性", prompts[0]) # 読む前の予想には本文を見せない | |
| self.assertIn("難しくて退屈そう", prompts[1]) # 予想を覚えたまま比べる | |
| self.assertEqual(row["予想"], "数学の記事なので難しくて退屈そうだと予想する。") | |
| self.assertEqual(kanjou.saikin(1)[0]["言葉"], ["意外"]) | |
| self.assertIn("表現論", kanjou.kaita_dai()) | |
| class OnceTest(unittest.TestCase): | |
| """もっと知りたい/意外 と受け止めた記事は、つながる未読の題を次に読む(感情がふるまいを変える)。""" | |
| def test_feeling_changes_next_topics(self): | |
| import gakushuu | |
| with tempfile.TemporaryDirectory() as d, mock.patch.dict(os.environ, {"KERNEL_GAKUSHUU_DIR": d}): | |
| with gakushuu._db() as db: | |
| db.execute("INSERT INTO chishiki(title,text,source,url,added) VALUES(?,?,?,?,?)", | |
| ("表現論", "群の表現の理論。" * 30, "Wikipedia", "", "2026-10-04")) | |
| db.executemany("INSERT INTO tsunagari VALUES(?,?,?)", | |
| [("表現論", "群論", "リンク"), ("表現論", "指標理論", "リンク"), ("表現論", "表現論", "リンク")]) | |
| import nooto | |
| db = nooto._open(Path(d) / "chishiki.sqlite3") | |
| db.execute("INSERT INTO nooto VALUES(?,?,?,?,?,?)", ("表現論", "群の対称性を行列で表す。", 5, "[]", "t", "2026-10-04")) | |
| db.commit() | |
| db.close() | |
| answers = iter(["難しそう。", '{"予想": "難しそう", "実際": "面白い", "言葉": ["もっと知りたい"], "理由": "対称性が広く使われる", "次": "群論を読む"}']) | |
| row = gakushuu.kanjou_once(ask=lambda prompt: next(answers)) | |
| self.assertEqual(sorted(row["寄せた題"]), ["指標理論", "群論"]) | |
| state = json.loads((Path(d) / "state.json").read_text(encoding="utf-8")) | |
| self.assertEqual(sorted(x["題"] for x in state["次の題"][:2]), ["指標理論", "群論"]) | |
| self.assertIsNone(gakushuu.kanjou_once(ask=lambda prompt: "x")) # 10分に1回 | |
| self.assertIn("表現論", gakushuu.overview({})["気持ち"][0]["文"]) | |
| if __name__ == "__main__": | |
| unittest.main() | |