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/tests/test_he_eval.py from miutti/intel-mac-local-llm: direct link, hf CLI and curl.
- Browser
- Download file 2.31 kB
-
https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/tests/test_he_eval.py
- Command line
-
hf download hf://miutti/intel-mac-local-llm/source/kernel/tests/test_he_eval.py
-
curl -L -o test_he_eval.py https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/tests/test_he_eval.py
2.31 kB
| #!/usr/bin/env python3 | |
| import json | |
| import sys | |
| import tempfile | |
| import unittest | |
| from pathlib import Path | |
| sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "LocalAI改良/tools/ops/humaneval")) | |
| try: | |
| import he_eval # リポジトリの外(LocalAI改良/tools/ops/humaneval)にある。無い所では飛ばす | |
| except ImportError: | |
| he_eval = None | |
| class HumanEvalTests(unittest.TestCase): | |
| def _problem(self, task_id="HumanEval/0"): | |
| return { | |
| "task_id": task_id, | |
| "prompt": "def add(a, b):\n \"\"\"add\"\"\"\n", | |
| "entry_point": "add", | |
| "test": "def check(candidate):\n assert candidate(2, 3) == 5\n", | |
| } | |
| def test_missing_generation_stays_in_denominator(self): | |
| with tempfile.TemporaryDirectory() as directory: | |
| root = Path(directory) | |
| data = root / "data.jsonl" | |
| gen = root / "gen.jsonl" | |
| data.write_text(json.dumps(self._problem()) + "\n" + | |
| json.dumps(self._problem("HumanEval/1")) + "\n", | |
| encoding="utf-8") | |
| gen.write_text(json.dumps({ | |
| "task_id": "HumanEval/0", | |
| "raw": "```python\ndef add(a, b):\n return a+b\n```", | |
| }) + "\n", encoding="utf-8") | |
| summary, rows = he_eval.evaluate( | |
| data, gen, timeout=3, memory_mb=256, output_mb=1, | |
| sandbox=False) | |
| self.assertEqual(summary["data_n"], 2) | |
| self.assertEqual(summary["pass"], 1) | |
| self.assertEqual(summary["missing_generation_n"], 1) | |
| self.assertEqual(summary["accuracy_over_data"], 0.5) | |
| self.assertEqual(rows[1]["why"], "missing_generation") | |
| def test_timeout_is_reported(self): | |
| problem = self._problem() | |
| generation = {"task_id": problem["task_id"], | |
| "raw": "```python\ndef add(a,b):\n while True: pass\n```"} | |
| result = he_eval.run_case(problem, generation, timeout=0.2, | |
| memory_mb=256, output_mb=1, sandbox=False) | |
| self.assertFalse(result["pass"]) | |
| self.assertEqual(result["why"], "timeout") | |
| if __name__ == "__main__": | |
| unittest.main() | |