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

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

Llama new
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

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"

intel-mac-local-llm / source /MODEL.md

miutti

Source: kernel, desktop app, tools (snapshot of the GitHub repo)

df41178 verified 22 days ago

preview code

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モデルについて ── 何であって、何ではないか

「そのレイヤー、そのまま持ってきてないよね？」への答えは、今回も正直に書きます。これは公式配布のままのモデルではありません。

いま使っているもの

Qwen3.6-35B-A3B の UD-Q2_K_XL を元に、専門家と補助的な予測層を選んで外し、語彙の読み込みを絞った派生モデルです。公式の Qwen3.6 そのものではありません。

	元モデル	いまのモデル
配布名	Qwen3.6-35B-A3B-MTP-UD-Q2_K_XL	`Qwen3.6-35B-A3B-UD-Q2_K_XL-k160.gguf`
容量	約 11.7 GiB	約 7.7 GiB
MoE 専門家	256人	160人
標準の層・注意機構	—	触っていない
MTP	付属	補助的な MTP 層を外した
量子化	UD-Q2_K_XL	元の量子化のまま（再量子化していない）

専門家は校正文から作った imatrix の使用回数を層ごとに見て、よく使われるものを残しました。同数なら入力側の二乗和、さらに同じなら元の番号で選びます。256人から160人を残す手順は dougu/kezuru_tejun.py と dougu/senmonka_kezuru.py にあります。

標準の層や注意機構を組み替えたわけではありません。一方で、MTP の補助層は削除しています。語彙もすべて残したままではなく、圧縮版 llama.cpp の llama_patch/koukai.patch で必要な語彙を選び、99.99% に絞って読み込みます。これらの変更を含め、公式モデルと同一だとは主張しません。

削減後の測定値は1回ごとの結果です。10月3日の全41問は 36/41 と 38/41。条件と経緯は dougu/jikken/KEKKA.md、手順は docs/REPRO.md を参照してください。以前の30B版の説明は docs/archive/MODEL_qwen3-30b-reap96.md に残しています。