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/docs/REPRO.md from miutti/intel-mac-local-llm: direct link, hf CLI and curl.
- Browser
- Download file 2.74 kB
-
https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/docs/REPRO.md
- Command line
-
hf download hf://miutti/intel-mac-local-llm/source/docs/REPRO.md
-
curl -L -o REPRO.md https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/docs/REPRO.md
再現に要るもの
現行の160人版を作り、同じ種類の測定を行うための記録です。実測値の履歴は dougu/jikken/KEKKA.md を参照してください。
入力と道具
- Python:
python3 --versionで確認。この資料を作成した環境は Python 3.14.5。 - 元モデル:
Qwen3.6-35B-A3B-MTP-UD-Q2_K_XL.gguf(約 11.7 GiB)。配布元は Hugging Face の unsloth/Qwen3.6-35B-A3B-GGUF のUD-Q2_K_XL。 - llama.cpp: commit **
b31b71f**。この作業場のllama_patch/koukai.patchを当てて構築し、llama-serverとllama-imatrixを使う。パッチ内にも語彙選択などの実装がある。 - 派生モデル:
models/Qwen3.6-35B-A3B-UD-Q2_K_XL-k160.gguf(約 7.7 GiB)。 - SHA256(Claude が記入):
- 160人版:
a5166e02dc0fbf5b86b01dbc546f3f17c9316dba334fc762220bb4d725c5691b - 元モデル:
ed7cda7e38985b4fcff76475865135039641d2bfbac3c169df15ca770f37fb0c
- 160人版:
作成
元モデルを
~/LocalAI_mirror/models/Qwen3.6-35B-A3B-MTP-UD-Q2_K_XL.ggufに置き、b31b71f にパッチを適用して作った道具が~/LocalAI_mirror/llama-koukai/にある状態にする。校正文から imatrix を作り、192人版と160人版を作る。既定で一問・知識・全41問の確認も行う:
python3 dougu/kezuru_tejun.py --dan kezuruこの手順は
dougu/kezuru_tejun.pyが呼ぶdougu/senmonka_kezuru.pyに従う。層ごとの imatrix 使用数を基準に選び、MTP を外す。再量子化はしない。語彙を絞るパッチ設定を含め、サーバーを起動する。
削った.ggufは160人版のパスに置き換える:llama-server -m 削った.gguf -t 6 -ngl 0 -c 32768 -np 2 -kvu \ --no-cache-idle-slots -cram 512 -fa off --reasoning-format none語彙選択は
KOUKAI_VOCAB_KEEP=vocab_keep_9999_q36_ids.txt、先読みは--spec-type none。本番構成の詳細はAGENTS.mdを参照。
測定
- 全41問の公開回帰テスト:
python3 dougu/honban.py j。 - 知識25問:
python3 dougu/chishiki_wa.py --toi <知識問題JSONL> --model <モデル.gguf> --llama <llama-server>。 - 書く速さ:
python3 dougu/hayasa.py --model <モデル.gguf> --llama <llama-server> --conf 現行:<サーバーに足す引数> --wa。
問題・採点・実行条件の変更履歴も含め、1回の数字だけで一般化しないでください。モデルの SHA256 は空欄のままにしてあり、別途確認して記入します。