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_big_knowledge.py from miutti/intel-mac-local-llm: direct link, hf CLI and curl.
- Browser
- Download file 6.44 kB
-
https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/tests/test_big_knowledge.py
- Command line
-
hf download hf://miutti/intel-mac-local-llm/source/kernel/tests/test_big_knowledge.py
-
curl -L -o test_big_knowledge.py https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/tests/test_big_knowledge.py
6.44 kB
| 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() | |