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: 5,854 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 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""同じ条件で llama-server の速度を測る小さな再利用可能ハーネス。"""
from __future__ import annotations
import argparse
import datetime as dt
import json
import statistics
import sys
import time
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent))
from public_bench import ( # noqa: E402
HARNESS_VERSION,
LocalClient,
atomic_write,
collect_runtime_metadata,
collect_server_metadata,
redact_url,
)
PROMPTS = [
"Return only the next prime number after 100.",
"Write one short sentence explaining why the sky appears blue.",
"Compute 37 * 29. Return only the integer.",
"Return a Python function named square(x) that returns x*x, with no explanation.",
]
def percentile(values: list[float], q: float) -> float | None:
if not values:
return None
xs = sorted(values)
index = (len(xs) - 1) * q
low = int(index)
high = min(low + 1, len(xs) - 1)
return xs[low] + (xs[high] - xs[low]) * (index - low)
def write_json(path: Path, obj: dict) -> None:
atomic_write(path, json.dumps(obj, ensure_ascii=False, indent=2) + "\n")
def main() -> int:
ap = argparse.ArgumentParser()
ap.add_argument("--url", default="http://127.0.0.1:8080/v1/chat/completions")
ap.add_argument("--out-dir", default="")
ap.add_argument("--repeat", type=int, default=1)
ap.add_argument("--warmup", type=int, default=1)
ap.add_argument("--max-tokens", type=int, default=128)
ap.add_argument("--timeout", type=int, default=900)
ap.add_argument("--retries", type=int, default=2)
ap.add_argument("--retry-backoff", type=float, default=0.25)
ap.add_argument("--seed", type=int, default=20260909)
ap.add_argument("--think", action="store_true")
args = ap.parse_args()
if args.repeat <= 0 or args.warmup < 0:
ap.error("--repeat は正数、--warmup は0以上が必要です")
stamp = dt.datetime.now().strftime("%Y%m%d-%H%M%S")
out = Path(args.out_dir or Path(__file__).resolve().parent / "bench_results" /
f"speed-{stamp}")
out.mkdir(parents=True, exist_ok=True)
client = LocalClient(args.url, args.think, args.max_tokens, args.timeout,
retries=args.retries, retry_backoff=args.retry_backoff,
seed=args.seed)
for i in range(args.warmup):
result = client.call(PROMPTS[i % len(PROMPTS)], f"warmup/{i}")
if result.get("error"):
print(f"warmup error: {result['error']}", file=sys.stderr)
rows = []
started = time.monotonic()
for repeat in range(args.repeat):
for prompt_index, prompt in enumerate(PROMPTS):
rid = f"p{prompt_index}/r{repeat}"
result = client.call(prompt, rid)
rows.append({
"id": rid,
"prompt_index": prompt_index,
"repeat": repeat,
"elapsed_sec": result.get("elapsed_sec", 0),
"timings": result.get("timings") or {},
"usage": result.get("usage"),
"model": result.get("model"),
"error": result.get("error"),
"error_type": result.get("error_type"),
"http_status": result.get("http_status"),
"attempts": result.get("attempts") or [],
"retry_count": result.get("retry_count", 0),
})
print(f" {rid}: {rows[-1]['elapsed_sec']:.3f}s "
f"{rows[-1]['error'] or 'ok'}", flush=True)
successful = [r for r in rows if not r.get("error")]
elapsed = [float(r.get("elapsed_sec", 0)) for r in successful]
tps = [float((r.get("timings") or {}).get("predicted_per_second", 0) or 0)
for r in successful if (r.get("timings") or {}).get("predicted_per_second")]
prompt_tps = [float((r.get("timings") or {}).get("prompt_per_second", 0) or 0)
for r in successful if (r.get("timings") or {}).get("prompt_per_second")]
summary = {
"harness_version": HARNESS_VERSION,
"created_at": dt.datetime.now(dt.timezone.utc).isoformat(),
"url": redact_url(args.url),
"repeat": args.repeat,
"warmup": args.warmup,
"n": len(rows),
"successful_n": len(successful),
"failed_n": len(rows) - len(successful),
"elapsed_total_sec": time.monotonic() - started,
"mean_sec": statistics.mean(elapsed) if elapsed else None,
"median_sec": statistics.median(elapsed) if elapsed else None,
"p95_sec": percentile(elapsed, 0.95),
"mean_predicted_tps": statistics.mean(tps) if tps else None,
"median_predicted_tps": statistics.median(tps) if tps else None,
"mean_prompt_tps": statistics.mean(prompt_tps) if prompt_tps else None,
"error_types": {
str(k): sum(1 for r in rows if (r.get("error_type") or "error") == k)
for k in sorted({r.get("error_type") or "error" for r in rows
if r.get("error")})
},
"runtime": collect_runtime_metadata(),
"server": collect_server_metadata(args.url),
}
jsonl = out / "speed.jsonl"
atomic_write(jsonl, "".join(json.dumps(r, ensure_ascii=False) + "\n" for r in rows))
write_json(out / "speed.summary.json", summary)
write_json(out / "config.json", {
"harness_version": HARNESS_VERSION,
"argv": sys.argv,
"url": redact_url(args.url),
"max_tokens": args.max_tokens,
"think": args.think,
"seed": args.seed,
"repeat": args.repeat,
"warmup": args.warmup,
})
print(json.dumps(summary, ensure_ascii=False, indent=2))
print(f"結果: {out}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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