Instructions to use Tyesee/ZuperAI-14B 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 Tyesee/ZuperAI-14B 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 Tyesee/ZuperAI-14B:Q4_K_M # Run inference directly in the terminal: llama cli -hf Tyesee/ZuperAI-14B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Tyesee/ZuperAI-14B:Q4_K_M # Run inference directly in the terminal: llama cli -hf Tyesee/ZuperAI-14B:Q4_K_M
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 Tyesee/ZuperAI-14B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Tyesee/ZuperAI-14B:Q4_K_M
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 Tyesee/ZuperAI-14B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Tyesee/ZuperAI-14B:Q4_K_M
Use Docker
docker model run hf.co/Tyesee/ZuperAI-14B:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Tyesee/ZuperAI-14B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Tyesee/ZuperAI-14B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Tyesee/ZuperAI-14B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Tyesee/ZuperAI-14B:Q4_K_M
- Ollama
How to use Tyesee/ZuperAI-14B with Ollama:
ollama run hf.co/Tyesee/ZuperAI-14B:Q4_K_M
- Unsloth Desktop
- Pi
How to use Tyesee/ZuperAI-14B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Tyesee/ZuperAI-14B:Q4_K_M
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": "Tyesee/ZuperAI-14B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Tyesee/ZuperAI-14B with Docker Model Runner:
docker model run hf.co/Tyesee/ZuperAI-14B:Q4_K_M
- Lemonade
How to use Tyesee/ZuperAI-14B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Tyesee/ZuperAI-14B:Q4_K_M
Run and chat with the model
lemonade run user.ZuperAI-14B-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Tyesee/ZuperAI-14B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Tyesee/ZuperAI-14B:Q4_K_M
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 Tyesee/ZuperAI-14B:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Tyesee/ZuperAI-14B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Tyesee/ZuperAI-14B:Q4_K_M
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 "Tyesee/ZuperAI-14B:Q4_K_M" \ --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 toolkit/verify_util.py from Tyesee/ZuperAI-14B: direct link, hf CLI and curl.
- Browser
- Download file 2.29 kB
-
https://huggingface.co/Tyesee/ZuperAI-14B/resolve/main/toolkit/verify_util.py
- Command line
-
hf download hf://Tyesee/ZuperAI-14B/toolkit/verify_util.py
-
curl -L -o verify_util.py https://huggingface.co/Tyesee/ZuperAI-14B/resolve/main/toolkit/verify_util.py
2.29 kB
| # -*- coding: utf-8 -*- | |
| """Общие утилиты исполняемой самопроверки (используют verify.py и webui.py).""" | |
| import os | |
| import subprocess | |
| import sys | |
| TEST_SYSTEM = ("Ты — инженер по тестированию. Пиши исчерпывающие юнит-тесты на стандартной " | |
| "библиотеке unittest. Тесты должны реально проверять корректность решения, " | |
| "включая крайние случаи. Не пиши реализацию — только тесты.") | |
| VERIFY_ATTEMPTS = 5 | |
| VERIFY_TIMEOUT = 30 | |
| def run_tests(code, tests, workdir, timeout=VERIFY_TIMEOUT): | |
| """Пишет код и тесты в песочницу и гоняет unittest. Возвращает (ok, вывод).""" | |
| with open(os.path.join(workdir, "solution.py"), "w", encoding="utf-8") as f: | |
| f.write(code) | |
| with open(os.path.join(workdir, "test_solution.py"), "w", encoding="utf-8") as f: | |
| f.write(tests) | |
| try: | |
| r = subprocess.run( | |
| [sys.executable, "-m", "unittest", "test_solution", "-v"], | |
| cwd=workdir, capture_output=True, text=True, timeout=timeout, | |
| ) | |
| out = (r.stdout or "") + "\n" + (r.stderr or "") | |
| return r.returncode == 0, out.strip() | |
| except subprocess.TimeoutExpired: | |
| return False, f"ТЕСТЫ ЗАВИСЛИ (таймаут {timeout}с)" | |
| def looks_like_tests(text): | |
| """Эвристика: ответ модели — это тесты или код?""" | |
| return ("import unittest" in text or "unittest" in text | |
| or "class Test" in text or "def test_" in text | |
| or ("assert" in text and "def " not in text)) | |
| def tail(s, n=900): | |
| """Хвост вывода — самое важное (финальный трейсбек).""" | |
| s = s.strip() | |
| return s if len(s) <= n else "…\n" + s[-n:] | |
| def ensure_unittest_header(tests): | |
| """Если модель забыла импорт unittest — добавляем заглушку-заголовок.""" | |
| if "import unittest" not in tests and "unittest" not in tests: | |
| return "import unittest\nfrom solution import *\n\n" + tests | |
| return tests | |