Text Generation
GGUF
Japanese
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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: 4,558 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
difflook.py -- なにが変わったかを、行ごとに見せる
2つの見せ方を持つ。
① フォルダの差 … 実行の前と後で、どのファイルが増えた・減った・動いた
② ファイルの中身 … 1枚のテキストの、どの行が変わったか
②は Python が持っている difflib をそのまま使う。
自分で書き直す理由がない(車輪の再発明はしない)。
"""
import difflib, os
# 中身を読んで比べてよい大きさ。これ以上は行数だけ見る
MAX_READ = 2_000_000
TEXT_EXT = {".txt", ".md", ".html", ".htm", ".css", ".js", ".json", ".py",
".csv", ".log", ".xml", ".yml", ".yaml", ".sh", ".ini", ".cfg"}
def is_text(path):
if os.path.splitext(path)[1].lower() in TEXT_EXT:
return True
try:
with open(path, "rb") as f:
head = f.read(2048)
return b"\0" not in head
except OSError:
return False
def read(path):
try:
if os.path.getsize(path) > MAX_READ:
return None
with open(path, encoding="utf-8", errors="replace") as f:
return f.read().splitlines()
except OSError:
return None
def lines(a_lines, b_lines, a_name="前", b_name="後", ctx=3):
"""行の差を [(しるし, 文)] で返す。
しるし: "+" 増えた / "-" 減った / " " そのまま / "@" 区切り
"""
if a_lines is None or b_lines is None:
return [("@", "中身が読めませんでした")]
out = []
for ln in difflib.unified_diff(a_lines, b_lines,
fromfile=a_name, tofile=b_name,
lineterm="", n=ctx):
if ln.startswith("+++") or ln.startswith("---"):
continue
if ln.startswith("@@"):
out.append(("@", ln.strip()))
elif ln.startswith("+"):
out.append(("+", ln[1:]))
elif ln.startswith("-"):
out.append(("-", ln[1:]))
else:
out.append((" ", ln[1:] if ln.startswith(" ") else ln))
if not out:
out = [("@", "中身は変わっていません")]
return out
def files(a_path, b_path, ctx=3):
"""2つのファイルの中身を比べる"""
if not os.path.exists(a_path):
return [("@", f"ありません: {a_path}")]
if not os.path.exists(b_path):
return [("@", f"ありません: {b_path}")]
if not (is_text(a_path) and is_text(b_path)):
sa, sb = os.path.getsize(a_path), os.path.getsize(b_path)
return [("@", f"文字のファイルではありません({sa:,} → {sb:,} バイト)")]
return lines(read(a_path), read(b_path),
os.path.basename(a_path), os.path.basename(b_path), ctx)
def folders(before, after):
"""フォルダの前と後(virtual.simulate の「前」「後」)を突き合わせる。
戻り値: [{"場所", "増えた": [...], "減った": [...], "件数": (前, 後)}]
"""
out = []
for d in sorted(set(before) | set(after)):
b = list(before.get(d, []))
a = list(after.get(d, []))
if b == a:
continue
out.append({"場所": d,
"増えた": [x for x in a if x not in b],
"減った": [x for x in b if x not in a],
"件数": (len(b), len(a))})
return out
def render(rows, limit=200):
"""行の差を、人が読める文字列にする"""
out = []
for i, (mark, text) in enumerate(rows):
if i >= limit:
out.append(f"… ほか {len(rows)-limit} 行")
break
out.append((" " if mark == " " else mark + " ") + text)
return "\n".join(out)
def render_folders(rows):
out = []
for r in rows:
name = os.path.basename(r["場所"]) or r["場所"]
out.append(f"{name}/ {r['件数'][0]} 件 → {r['件数'][1]} 件")
for x in r["減った"]:
out.append(" - " + x)
for x in r["増えた"]:
out.append(" + " + x)
return "\n".join(out) or "変わったところはありません"
if __name__ == "__main__":
import sys
if len(sys.argv) == 3:
print(render(files(sys.argv[1], sys.argv[2])))
else:
a = ["こんにちは", "ここは変わらない", "古い行", "おわり"]
b = ["こんにちは", "ここは変わらない", "新しい行", "足した行", "おわり"]
print(render(lines(a, b)))
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