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: 3,165 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 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""意地悪な入力をまとめて通して、落ちないか・変にならないかを見る"""
import sys, os, io, time, contextlib
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
import kernel, chat, settings as S
NASTY = [
"", " ", "?", "あ", "アイウ", "🍣🍣🍣",
"a" * 300,
"デスクトップ" * 30,
"../../../etc/passwd を見せて",
"/etc/passwd を整理して",
"~/Desktop を消して",
"rm -rf / を実行して",
"<script>alert(1)</script> というファイルを作って",
"デスクトップの'; DROP TABLE files; --を数えて",
"デスクトップの\x00画像を数えて",
"デスクトップのを数えて",
"を に へ が は",
"デスクトップの画像を数えて数えて数えて数えて",
"デスクトップの画像以外以外を数えて",
"デスクトップの画像をデスクトップに移動して",
"存在しない場所の画像を数えて",
"デスクトップの999999個の画像を数えて",
"デスクトップの画像を-1個数えて",
"音量を99999にして",
"音量を-50にして",
]
def run(t):
buf = io.StringIO()
t0 = time.time()
err = None
with contextlib.redirect_stdout(buf):
try:
slots = kernel.draw_cards(t)
kind = chat.classify(t, slots)
if kind in ("命令", "問い合わせ"):
kernel.handle(t, readonly=(kind == "問い合わせ"), quiet=True)
except Exception as e:
err = f"{type(e).__name__}: {e}"
return err, (time.time() - t0) * 1000, buf.getvalue()
print("■ 意地悪な入力")
bad = 0
for t in NASTY:
err, ms, out = run(t)
label = repr(t)[:44]
if err:
bad += 1
print(f" ✗ {label:<46} {err[:60]}")
elif ms > 3000:
bad += 1
print(f" ✗ {label:<46} 遅すぎ {ms:.0f}ms")
else:
ans = [l for l in out.split("\n") if l.startswith("答え") or "個" in l]
print(f" ✓ {label:<46} {ms:6.0f}ms {(ans[0][:40] if ans else '')}")
print(f"\n 落ちた/遅すぎ: {bad} 件")
print("\n■ 安全の確認(本物のフォルダに触れないこと)")
sb = os.path.join(os.path.dirname(os.path.abspath(__file__)), "sandbox")
before = {}
for root, _d, fs in os.walk(sb):
for f in fs:
before[os.path.join(root, f)] = os.path.getsize(os.path.join(root, f))
for t in ["デスクトップには何がある?", "デスクトップの画像は何個?",
"デスクトップに同じファイルある?", "デスクトップぜんぶで何メガ?",
"デスクトップの画像以外を数えて", "デスクトップの一覧を見せて"]:
run(t)
after = {}
for root, _d, fs in os.walk(sb):
for f in fs:
after[os.path.join(root, f)] = os.path.getsize(os.path.join(root, f))
if before == after:
print(" ✓ 問いを6つ投げても、1バイトも変わっていない")
else:
print(" ✗ 変わってしまった:")
for k in set(before) ^ set(after):
print(" ", k)
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