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"
File size: 2,741 Bytes
df41178 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | # 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`](../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](https://huggingface.co/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`](../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`
## 作成
1. 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。
2. 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:
```bash
python3 dougu/kezuru_tejun.py --dan kezuru
```
この手順は [`dougu/kezuru_tejun.py`](../dougu/kezuru_tejun.py) が呼ぶ [`dougu/senmonka_kezuru.py`](../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。
3. 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:
```bash
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`](../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。
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