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: 2,970 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 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
feedback.py -- 気づいたことを、その場で書いて残す(虫マーク)
「おかしい」と思ったときに、いちいち人に説明しに行くのは面倒。
その場で書いて専用のフォルダに落としておけば、あとでまとめて読める。
1件 = マークダウン1枚。そのとき何が起きていたかも一緒に残す
(直前の入力・出力・経過・設定・版)。あとから再現できるように。
"""
import json, os, time, platform
DIR = os.path.join(os.path.expanduser("~"), "Library",
"Application Support", "kernel-ai", "フィードバック")
KINDS = ["不具合", "こうしてほしい", "使いにくい", "その他"]
def _ensure():
os.makedirs(DIR, exist_ok=True)
return DIR
def add(text, kind="不具合", context=None):
"""1件書き足す。戻り値はできたファイルのパス"""
text = (text or "").strip()
if not text:
raise ValueError("中身がからっぽです")
if kind not in KINDS:
kind = "その他"
_ensure()
t = time.localtime()
stamp = time.strftime("%Y%m%d-%H%M%S", t)
path = os.path.join(DIR, f"{stamp}_{kind}.md")
ctx = context or {}
lines = [
f"# {kind}",
f"- 日時: {time.strftime('%Y-%m-%d %H:%M:%S', t)}",
f"- 環境: macOS {platform.mac_ver()[0]} / Python {platform.python_version()}",
f"- モード: {ctx.get('モード', '—')}",
"",
"## 書いたこと",
text,
"",
]
if ctx.get("入力"):
lines += ["## そのときの入力", "```", str(ctx["入力"]), "```", ""]
if ctx.get("出力"):
lines += ["## そのときの返事", "```", str(ctx["出力"])[:4000], "```", ""]
if ctx.get("経過"):
lines += ["## そのときの経過", "```", str(ctx["経過"])[:8000], "```", ""]
if ctx.get("設定"):
lines += ["## そのときの設定", "```json",
json.dumps(ctx["設定"], ensure_ascii=False, indent=1), "```", ""]
with open(path, "w", encoding="utf-8") as f:
f.write("\n".join(lines))
return path
def listing(limit=50):
_ensure()
out = []
for fn in sorted(os.listdir(DIR), reverse=True)[:limit]:
if not fn.endswith(".md"):
continue
p = os.path.join(DIR, fn)
head = ""
try:
with open(p, encoding="utf-8") as f:
for ln in f:
if ln.startswith("## 書いたこと"):
head = next(f, "").strip()
break
except Exception:
pass
out.append({"名": fn, "パス": p, "さわり": head[:60]})
return out
def folder():
return _ensure()
if __name__ == "__main__":
print("置き場:", folder())
for f in listing():
print(" -", f["名"], "|", f["さわり"])
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