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/chosuichi.py from miutti/intel-mac-local-llm: direct link, hf CLI and curl.
- Browser
- Download file 3.9 kB
-
https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/chosuichi.py
- Command line
-
hf download hf://miutti/intel-mac-local-llm/source/kernel/chosuichi.py
-
curl -L -o chosuichi.py https://huggingface.co/miutti/intel-mac-local-llm/resolve/main/source/kernel/chosuichi.py
3.9 kB
| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| """ | |
| chosuichi.py -- セルオートマトンの貯水池(Reservoir / ReCA) | |
| 【ねらい】 | |
| 「時間の流れ」を、学習なしで ほどく道具。 | |
| 中身は、一列に並んだマスが XOR で書き変わっていくだけ。 | |
| 学ぶのは、出口の一段だけ(ここはツェットリン機械にやらせる)。 | |
| 【なぜ効くのか】 | |
| 入れたもの → 規則90 で何段も広げる | |
| → 最初のわずかな違いが、段が進むほど大きく開く | |
| → もともと混ざっていたものが、離れて置き直される | |
| ばらばらにするのは ただの XOR。掛け算はしない。 | |
| 【カーネルでの使いどころ】 | |
| 音の並び、押した順、字の並び ── 順番が意味を持つもの。 | |
| いまのカーネルは「順番」をほとんど見ていない。 | |
| """ | |
| import os, random, sys | |
| sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) | |
| def kisoku90(s, n): | |
| """規則90: 自分は消え、左右の XOR になる""" | |
| hidari = ((s << 1) | (s >> (n - 1))) & ((1 << n) - 1) # 輪にする | |
| migi = (s >> 1) | ((s & 1) << (n - 1)) | |
| return hidari ^ migi | |
| def kisoku150(s, n): | |
| """規則150: 左 XOR 自分 XOR 右。90 より混ざり方が濃い""" | |
| hidari = ((s << 1) | (s >> (n - 1))) & ((1 << n) - 1) | |
| migi = (s >> 1) | ((s & 1) << (n - 1)) | |
| return hidari ^ s ^ migi | |
| class Chosuichi: | |
| def __init__(self, haba=64, dan=8, kisoku=kisoku90): | |
| self.haba, self.dan, self.kisoku = haba, dan, kisoku | |
| def hirogeru(self, tane): | |
| """一つの入れものを、段ごとに広げて つなげた ビット列にする""" | |
| n, s = self.haba, tane & ((1 << self.haba) - 1) | |
| out, sh = 0, 0 | |
| for _ in range(self.dan): | |
| s = self.kisoku(s, n) | |
| out |= s << sh | |
| sh += n | |
| return out | |
| def hirosa(self): | |
| return self.haba * self.dan | |
| def nagare(self, tsubu): | |
| """順番に来るものを、混ぜながら流し込む(前の状態を持ち越す)""" | |
| n = self.haba | |
| s = 0 | |
| ato = 0 | |
| for t in tsubu: | |
| s ^= (t & ((1 << n) - 1)) # 新しい入力を混ぜる | |
| for _ in range(self.dan): | |
| s = self.kisoku(s, n) | |
| ato ^= s # 通った跡を残す | |
| return ato | |
| def tameshi(): | |
| import tsetlin | |
| print("■ ばらばらにする道具として役に立つか\n") | |
| rnd = random.Random(0) | |
| # 課題: 7ビットの偶奇(ぜんぶの XOR)。 | |
| # 「かつ/でない」の式ひとつでは絶対に書けない、いちばん意地悪な問題 | |
| N = 7 | |
| D = [(b, bin(b).count("1") % 2) for b in range(1 << N)] | |
| rnd.shuffle(D) | |
| tr, te = D[:100], D[100:] | |
| def hakaru(x_of, hirosa, namae): | |
| m = tsetlin.Kikai(hirosa, kata=60, T=12, s=4.0) | |
| for _ in range(60): | |
| rnd.shuffle(tr) | |
| for b, y in tr: | |
| m.manabu(x_of(b), y) | |
| ok = sum(1 for b, y in te if m.kotae(x_of(b)) == y) | |
| print(f" {namae}: {ok}/{len(te)} ({ok/len(te):.0%})") | |
| hakaru(lambda b: b, N, "そのまま ") | |
| c = Chosuichi(haba=N, dan=6, kisoku=kisoku150) | |
| hakaru(c.hirogeru, c.hirosa, f"貯水池を通す({c.hirosa}ビットに広がる)") | |
| print("\n ── 順番を見分けられるか(同じ材料・並びだけ違う)──") | |
| c2 = Chosuichi(haba=32, dan=4) | |
| a = c2.nagare([1, 2, 4]) | |
| b = c2.nagare([4, 2, 1]) | |
| d = c2.nagare([1, 2, 4]) | |
| print(f" [1,2,4] と [4,2,1] : 違うビット {bin(a ^ b).count('1')} 個") | |
| print(f" [1,2,4] と [1,2,4] : 違うビット {bin(a ^ d).count('1')} 個" | |
| " ← 同じ並びなら ぴったり同じ") | |
| if __name__ == "__main__": | |
| tameshi() | |