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: 6,437 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 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | 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]))
sys.path.insert(1, str(Path(__file__).resolve().parents[2]))
import gakushuu
import kazoeru
def make_box(path, article):
with sqlite3.connect(path) as db:
db.execute("CREATE VIRTUAL TABLE chishiki USING fts5(title,text,source UNINDEXED,url UNINDEXED,added UNINDEXED)")
db.execute("CREATE TABLE tsunagari(moto TEXT NOT NULL,saki TEXT NOT NULL,shurui TEXT NOT NULL,UNIQUE(moto,saki,shurui))")
db.execute("INSERT INTO chishiki(title,text,source,url,added) VALUES(?,?,?,?,?)", (article, article + " 本文", "test", "", "now"))
class BigKnowledgeTests(unittest.TestCase):
def test_title_index_edges_and_refetch_skip(self):
with tempfile.TemporaryDirectory() as tmp, mock.patch.dict(os.environ, {"KERNEL_GAKUSHUU_DIR": tmp}):
with gakushuu._db() as db:
for title, body in (("銀河系", "渦巻き星雲の本文"), ("青色星", "銀河系と宇宙の本文")):
cur = db.execute("INSERT INTO chishiki(title,text,source,url,added) VALUES(?,?,?,?,?)", (title, body, "test", "", "now"))
db.execute("INSERT INTO daimei(title,id) VALUES(?,?)", (title, cur.lastrowid))
gakushuu._add_trigram(db, cur.lastrowid, title, body, "test")
if db.execute("SELECT 1 FROM sqlite_master WHERE name='daimei_trigram'").fetchone():
db.execute("INSERT INTO daimei_trigram(rowid,title) VALUES(?,?)", (cur.lastrowid, title))
gakushuu._add_article_edges(db, title, body)
self.assertTrue(db.execute("SELECT 1 FROM tsunagari WHERE moto='青色星' AND saki='銀河系'").fetchone())
self.assertTrue(db.execute("SELECT 1 FROM tsunagari WHERE moto='青色星' AND saki='銀河系' AND shurui='本文'").fetchone())
db.execute("INSERT OR REPLACE INTO chishiki_meta(k,v) VALUES('取り直し不要','1')")
self.assertEqual(gakushuu._names(), {"青色星", "銀河系"})
self.assertFalse(gakushuu._refetch_one({}, object()))
with gakushuu._db() as db:
self.assertTrue(gakushuu._delete_article(db, "青色星"))
self.assertFalse(db.execute("SELECT 1 FROM daimei WHERE title='青色星'").fetchone())
self.assertFalse(db.execute("SELECT 1 FROM tsunagari WHERE moto='青色星' OR saki='青色星'").fetchone())
def test_large_unmarked_box_gets_deferred_full_rebuild(self):
with tempfile.TemporaryDirectory() as tmp, mock.patch.dict(os.environ, {"KERNEL_GAKUSHUU_DIR": tmp}):
with gakushuu._db() as db:
db.executemany("INSERT INTO chishiki(title,text,source,url,added) VALUES(?,?,?,?,?)",
((f"題{i}", "本文", "test", "", "now") for i in range(20001)))
db.execute("DELETE FROM chishiki_meta WHERE k='枝再構築'")
gakushuu._ensure_knowledge_indexes(db)
marker = db.execute("SELECT v FROM chishiki_meta WHERE k='枝再構築'").fetchone()[0]
self.assertEqual(marker, "省略:20000超")
def test_imported_bytes_are_not_charged_as_growth(self):
with tempfile.TemporaryDirectory() as tmp, mock.patch.dict(os.environ, {"KERNEL_GAKUSHUU_DIR": tmp}):
with gakushuu._db() as db:
db.execute("INSERT OR REPLACE INTO chishiki_meta(k,v) VALUES('取り込み時の大きさ','999999999')")
self.assertEqual(gakushuu.used_bytes(), 0)
def test_count_or_calculation_signal_exists(self):
self.assertTrue(kazoeru.aizu("このフォルダのファイルを何個数えて"))
self.assertTrue(kazoeru.aizu("12 * 8 を計算して"))
self.assertFalse(kazoeru.aizu("おはよう"))
def test_prepare_switch_restore_round_trip(self):
import importlib.util
script = Path(__file__).resolve().parents[2] / "dougu" / "chishiki_irekae.py"
spec = importlib.util.spec_from_file_location("chishiki_irekae_test", script)
swap = importlib.util.module_from_spec(spec)
spec.loader.exec_module(swap)
with tempfile.TemporaryDirectory() as tmp:
root = Path(tmp)
new, prod = root / "new.sqlite3", root / "chishiki.sqlite3"
make_box(new, "新しい記事")
make_box(prod, "古い記事")
with sqlite3.connect(new) as db:
db.execute("CREATE TABLE teian(id INTEGER PRIMARY KEY,題 TEXT)")
db.execute("INSERT INTO teian VALUES(1,'新提案')")
with sqlite3.connect(prod) as db:
db.execute("CREATE TABLE nooto(title TEXT PRIMARY KEY,youten TEXT NOT NULL,omoshirosa INTEGER NOT NULL,tsunagari TEXT NOT NULL,model TEXT NOT NULL,added TEXT NOT NULL)")
db.execute("INSERT INTO nooto VALUES('古い記事','要点',4,'枝','m','now')")
db.execute("CREATE TABLE teian(id INTEGER PRIMARY KEY,題 TEXT)")
db.execute("INSERT INTO teian VALUES(1,'提案')")
db.execute("INSERT INTO tsunagari VALUES('古い記事','新しい記事','test')")
swap.junbi(new, prod)
with mock.patch.object(swap, "_server_running", return_value=False):
swap.i_rekae(prod)
with sqlite3.connect(prod) as db:
titles = {row[0] for row in db.execute("SELECT title FROM chishiki")}
self.assertEqual(titles, {"新しい記事", "古い記事"})
self.assertEqual(db.execute("SELECT youten FROM nooto WHERE title='古い記事'").fetchone()[0], "要点")
self.assertEqual({r[0] for r in db.execute("SELECT 題 FROM teian")}, {"新提案", "提案"})
self.assertGreater(int(db.execute("SELECT v FROM chishiki_meta WHERE k='取り込み時の大きさ'").fetchone()[0]), 0)
with mock.patch.object(swap, "_server_running", return_value=False):
swap.modosu(prod)
with sqlite3.connect(prod) as db:
self.assertEqual(db.execute("SELECT title FROM chishiki").fetchone()[0], "古い記事")
self.assertTrue(Path(str(prod) + ".irekae_shippai").is_file())
if __name__ == "__main__":
unittest.main()
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