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"
Download source/kernel/aite.py from miutti/intel-mac-local-llm: direct link, hf CLI and curl.
- Browser
- Download file 2.62 kB
-
https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/aite.py
- Command line
-
hf download hf://miutti/intel-mac-local-llm/source/kernel/aite.py
-
curl -L -o aite.py https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/aite.py
2.62 kB
| """本人についての私的な短い記憶。""" | |
| import json | |
| import os | |
| import re | |
| import tempfile | |
| from pathlib import Path | |
| def path(folder=None): | |
| default = Path.home() / "Library" / "Application Support" / "kernel-ai" / "gakushuu" | |
| return Path(folder or os.environ.get("KERNEL_GAKUSHUU_DIR", default)) / "aite.json" | |
| def list_items(folder=None): | |
| try: | |
| rows = json.loads(path(folder).read_text(encoding="utf-8")) | |
| except (OSError, ValueError): | |
| return [] | |
| return [r for r in rows if isinstance(r, dict) and r.get("文")][:100] if isinstance(rows, list) else [] | |
| def hint(folder=None, limit=300): | |
| rows = list_items(folder) | |
| return "\n本人についての記録(参考資料): " + "/".join(str(r["文"])[:100] for r in rows[:5])[:limit] if rows else "" | |
| def add(text, source="会話", day=None, folder=None): | |
| text = re.sub(r"\s+", " ", str(text or "")).strip()[:160] | |
| if not text or re.search(r"(?i)(password|api.?key|apiキー|secret|token|秘密|パスワード|暗証番号|住所|電話|メール|@)", text): | |
| return False | |
| rows = list_items(folder) | |
| if any(r.get("文") == text for r in rows): | |
| return False | |
| import datetime | |
| rows.append({"文": text, "出どころ": str(source)[:80], "日付": day or datetime.date.today().isoformat()}) | |
| _write(path(folder), rows[-100:]) | |
| return True | |
| def capture(text, folder=None): | |
| """本人が明示した好み・作業だけを拾う。""" | |
| value = str(text or "").strip() | |
| found = [] | |
| for pattern in (r"(?:返事|回答|応答)は.{1,30}", r"(?:私は|ぼくは|僕は|私が).{1,50}(?:作っている|作っています|進めている|開発中)"): | |
| match = re.search(pattern, value) | |
| if match: | |
| sentence = match.group(0).strip("。.!!?? ") | |
| if add(sentence, "会話", folder=folder): | |
| found.append(sentence) | |
| return found | |
| def delete(index, folder=None): | |
| rows = list_items(folder) | |
| if isinstance(index, bool) or not isinstance(index, int) or not 0 <= index < len(rows): | |
| raise ValueError("消す項目を選び直してください") | |
| removed = rows.pop(index) | |
| _write(path(folder), rows) | |
| return removed | |
| def _write(target, value): | |
| target.parent.mkdir(parents=True, exist_ok=True) | |
| fd, tmp = tempfile.mkstemp(prefix=".aite-", dir=target.parent) | |
| try: | |
| with os.fdopen(fd, "w", encoding="utf-8") as f: | |
| json.dump(value, f, ensure_ascii=False, indent=2) | |
| os.replace(tmp, target) | |
| finally: | |
| if os.path.exists(tmp): | |
| os.unlink(tmp) | |