Text Generation
GGUF
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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: 8,438 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""ベンチマーク結果を同じ表にまとめ、データ選抜の一致も確認する。"""
from __future__ import annotations
import argparse
import datetime as dt
import json
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent))
from public_bench import HARNESS_VERSION, atomic_write, sha256_file # noqa: E402
def load_json(path: Path) -> dict:
return json.loads(path.read_text(encoding="utf-8"))
def parse_run(value: str) -> tuple[str, Path]:
if "=" not in value:
raise ValueError(f"--run は LABEL=DIR 形式です: {value}")
label, directory = value.split("=", 1)
if not label or not directory:
raise ValueError(f"--run は LABEL=DIR 形式です: {value}")
return label, Path(directory)
def run_summary(directory: Path) -> dict:
summary_path = directory / "summary.json"
base = load_json(summary_path) if summary_path.exists() else {}
by_name = {x.get("name"): x for x in base.get("summaries", [])
if isinstance(x, dict) and x.get("name")}
eval_summaries = []
for path in sorted(directory.glob("*.summary.json")):
if path.name in {"speed.summary.json"}:
continue
try:
item = load_json(path)
if path.name.endswith(".eval.summary.json") and item.get("data_n") is not None:
eval_summaries.append((path, item))
continue
if item.get("name"):
by_name[item["name"]] = item
except Exception:
continue
for path, item in eval_summaries:
# HumanEval の生成サマリーを、採点済みの pass@1 で上書きする。
current = by_name.get("humaneval", {})
by_name["humaneval"] = {
**current,
"name": "humaneval",
"n": item.get("data_n"),
"scored_n": item.get("data_n"),
"correct": item.get("pass"),
"accuracy": item.get("accuracy_over_data"),
"failed_n": item.get("fail"),
"eval_summary": str(path),
}
if not any(path.name.endswith("humaneval.eval.summary.json") for path, _ in eval_summaries):
detail = directory / "humaneval.eval.jsonl"
if detail.exists():
try:
rows = [json.loads(line) for line in detail.read_text(
encoding="utf-8").splitlines() if line.strip()]
current = by_name.get("humaneval", {})
passed = sum(bool(row.get("pass")) for row in rows)
by_name["humaneval"] = {
**current,
"name": "humaneval",
"n": len(rows),
"scored_n": len(rows),
"correct": passed,
"accuracy": passed / len(rows) if rows else None,
"failed_n": len(rows) - passed,
"eval_detail": str(detail),
}
except Exception:
pass
return {**base, "summaries": list(by_name.values())}
def manifests(directory: Path) -> dict[str, dict]:
out = {}
for path in directory.glob("*.manifest.json"):
try:
out[path.name.removesuffix(".manifest.json")] = load_json(path)
except Exception:
pass
return out
def make_report(runs: list[tuple[str, Path]]) -> tuple[dict, str]:
data = {"harness_version": HARNESS_VERSION, "created_at": dt.datetime.now(
dt.timezone.utc).isoformat(), "runs": {}}
bench_names = set()
for label, directory in runs:
run = run_summary(directory)
summaries = {x.get("name"): x for x in run.get("summaries", [])
if isinstance(x, dict) and x.get("name")}
manifests_data = manifests(directory)
data["runs"][label] = {
"directory": str(directory),
"summary": run,
"manifests": {
name: {
"selected_n": manifest.get("selected_n"),
"selected_rows_sha256": manifest.get("selected_rows_sha256"),
"manifest_sha256": sha256_file(directory / f"{name}.manifest.json"),
}
for name, manifest in manifests_data.items()
},
}
bench_names.update(summaries)
lines = [
"# ベンチマーク比較",
"",
f"- harness: `{HARNESS_VERSION}`",
f"- generated: `{data['created_at']}`",
"",
"| ベンチマーク | " + " | ".join(label for label, _ in runs) + " |",
"|---|" + "---:|" * len(runs),
]
for name in sorted(bench_names):
cells = []
for label, _ in runs:
summary = next((x for x in data["runs"][label]["summary"].get("summaries", [])
if x.get("name") == name), None)
if not summary:
cells.append("—")
continue
acc = summary.get("accuracy")
score = (f"{summary.get('correct')}/{summary.get('scored_n')} "
f"({100*acc:.1f}%)" if acc is not None else "未完了")
failed = summary.get("failed_n")
if failed is None:
file_name = summary.get("file")
if file_name and Path(file_name).exists():
try:
failed = sum(1 for line in Path(file_name).read_text(
encoding="utf-8").splitlines()
if line.strip() and (json.loads(line).get("error")
or not json.loads(line).get("content", "").strip()))
except Exception:
failed = None
if failed is None and summary.get("n") is not None and summary.get("scored_n") is not None:
failed = summary["n"] - summary["scored_n"]
cells.append(f"{score}; 失敗 {failed if failed is not None else '?'}")
lines.append(f"| {name} | " + " | ".join(cells) + " |")
lines += ["", "## データ選抜の一致", ""]
for name in sorted(bench_names):
entries = []
for label, _ in runs:
manifest = data["runs"][label]["manifests"].get(name)
entries.append(f"{label}={manifest.get('selected_rows_sha256') if manifest else 'なし'}")
lines.append(f"- `{name}`: " + ", ".join(entries))
speed_rows = []
for label, directory in runs:
path = directory / "speed.summary.json"
if path.exists():
speed_rows.append((label, load_json(path)))
if speed_rows:
lines += ["", "## 速度", "", "| 実行 | 平均秒 | 中央値秒 | p95秒 | 生成tok/s |", "|---|---:|---:|---:|---:|"]
for label, speed in speed_rows:
lines.append("| %s | %s | %s | %s | %s |" % (
label,
_fmt(speed.get("mean_sec")), _fmt(speed.get("median_sec")),
_fmt(speed.get("p95_sec")), _fmt(speed.get("median_predicted_tps"))))
return data, "\n".join(lines) + "\n"
def _fmt(value) -> str:
return "—" if value is None else f"{float(value):.2f}"
def main() -> int:
ap = argparse.ArgumentParser()
ap.add_argument("--run", action="append", required=True,
help="比較対象 LABEL=結果ディレクトリ(2つ以上推奨)")
ap.add_argument("--out", default="")
args = ap.parse_args()
try:
runs = [parse_run(value) for value in args.run]
except ValueError as exc:
ap.error(str(exc))
if len(runs) < 1:
ap.error("少なくとも1つの --run が必要です")
for _, directory in runs:
if not directory.exists():
ap.error(f"結果ディレクトリがありません: {directory}")
data, markdown = make_report(runs)
out = Path(args.out or f"benchmark-comparison-{dt.datetime.now().strftime('%Y%m%d-%H%M%S')}.md")
if out.suffix.lower() == ".json":
json_path = out
md_path = out.with_suffix(".md")
else:
md_path = out
json_path = out.with_suffix(".json")
atomic_write(md_path, markdown)
atomic_write(json_path, json.dumps(data, ensure_ascii=False, indent=2) + "\n")
print(f"Markdown: {md_path}\nJSON: {json_path}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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