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"
File size: 3,549 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 | 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()
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