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/tests/test_human_features.py from miutti/intel-mac-local-llm: direct link, hf CLI and curl.
- Browser
- Download file 3.55 kB
-
https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/tests/test_human_features.py
- Command line
-
hf download hf://miutti/intel-mac-local-llm/source/kernel/tests/test_human_features.py
-
curl -L -o test_human_features.py https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/tests/test_human_features.py
3.55 kB
| import json | |
| import tempfile | |
| import unittest | |
| from pathlib import Path | |
| import aite | |
| import loop | |
| import seiri | |
| import yarukoto | |
| class HumanFeaturesTest(unittest.TestCase): | |
| def test_loop_accepts_nfkc_and_japanese_units(self): | |
| self.assertEqual(loop.parse("/loop 30 新しい技術を作る"), | |
| {"topic": "新しい技術を作る", "interval": 1800}) | |
| self.assertEqual(loop.parse("/loop 30分 お題")["interval"], 1800) | |
| self.assertEqual(loop.parse("/loop 1時間 お題")["interval"], 3600) | |
| self.assertEqual(loop.parse("/loop 90秒 お題")["interval"], 90) | |
| self.assertEqual(loop.parse("/loop 30m お題")["interval"], 1800) | |
| def test_loop_start_message_explains_schedule(self): | |
| self.assertEqual(loop.start_message(1800, "新しい技術", hour=19), | |
| "わかりました。30分ごとに『新しい技術』を最大20周やります。1周目は今から。") | |
| self.assertIn("7時から", loop.start_message(3600, "題", hour=2)) | |
| def test_aite_is_private_bounded_and_deletable(self): | |
| with tempfile.TemporaryDirectory() as temp: | |
| self.assertTrue(aite.capture("返事は短くお願いします", temp)) | |
| self.assertEqual(len(aite.list_items(temp)), 1) | |
| self.assertFalse(aite.add("APIキーは秘密です", folder=temp)) | |
| removed = aite.delete(0, temp) | |
| self.assertIn("返事は短く", removed["文"]) | |
| self.assertEqual(aite.list_items(temp), []) | |
| def test_seiri_reviews_cards_and_writes_diary_without_llm(self): | |
| with tempfile.TemporaryDirectory() as temp: | |
| base = Path(temp) | |
| (base / "kyoukun.json").write_text(json.dumps([ | |
| {"知らせ": "同じ教訓", "強さ": 4, "次に見直す日": "2020-01-01"}, | |
| {"知らせ": "同じ教訓", "強さ": 4, "次に見直す日": "2020-01-01"}]), encoding="utf-8") | |
| result = seiri.run(folder=base, now=1791158400) | |
| cards = json.loads((base / "kyoukun.json").read_text(encoding="utf-8")) | |
| self.assertEqual(len(cards), 1) | |
| self.assertEqual(result["思い出した数"], 2) | |
| self.assertTrue((base / "nikki.jsonl").exists()) | |
| self.assertTrue((base / "seichou.json").exists()) | |
| def test_seiri_accepts_a_replaceable_bounded_summarizer(self): | |
| with tempfile.TemporaryDirectory() as temp: | |
| calls = [] | |
| def ask(prompt): | |
| calls.append(prompt) | |
| return {"相手": [{"文": "返事は短く"}]} | |
| result = seiri.run(folder=Path(temp), recent=[{"役": "user", "文": "これから整理"}], ask=ask) | |
| self.assertEqual(len(calls), 1) | |
| self.assertEqual(result["相手の数"], 1) | |
| self.assertIn("返事は短く", aite.list_items(temp)[0]["文"]) | |
| def test_yarukoto_respects_hourly_limit(self): | |
| with tempfile.TemporaryDirectory() as temp: | |
| base = Path(temp) | |
| (base / "state.json").write_text(json.dumps({"興味": [{"テーマ": "計算", "題": "計算", "問い": "なぜ?"}]}), encoding="utf-8") | |
| first = yarukoto.run_once(folder=base, now=100000) | |
| self.assertEqual(first["種類"], "問いに答える") | |
| self.assertIsNone(yarukoto.run_once(folder=base, now=100001)) | |
| self.assertEqual(len((base / "yarukoto.jsonl").read_text(encoding="utf-8").splitlines()), 1) | |
| if __name__ == "__main__": | |
| unittest.main() | |