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
- 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
- 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/MODEL.md from miutti/intel-mac-local-llm: direct link, hf CLI and curl.
- Browser
- Download file 2.09 kB
-
https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/MODEL.md
- Command line
-
hf download hf://miutti/intel-mac-local-llm/source/MODEL.md
-
curl -L -o MODEL.md https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/MODEL.md
2.09 kB
| # 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`](dougu/kezuru_tejun.py) と [`dougu/senmonka_kezuru.py`](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`](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`](dougu/jikken/KEKKA.md)、手順は [`docs/REPRO.md`](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`](docs/archive/MODEL_qwen3-30b-reap96.md) に残しています。 | |