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/project.py from Tyesee/ZuperAI-14B: direct link, hf CLI and curl.
- Browser
- Download file 3.37 kB
-
https://huggingface.co/Tyesee/ZuperAI-14B/resolve/main/toolkit/project.py
- Command line
-
hf download hf://Tyesee/ZuperAI-14B/toolkit/project.py
-
curl -L -o project.py https://huggingface.co/Tyesee/ZuperAI-14B/resolve/main/toolkit/project.py
3.37 kB
| # -*- coding: utf-8 -*- | |
| """Генерация полного проекта по ТЗ (№2): структура, файлы, тесты, прогон. | |
| Модель генерирует проект как JSON {"files": {"имя.py": "код", ...}}, пишет файлы | |
| в папку, затем гоняет unittest-тесты и при падении чинит. | |
| Запуск: | |
| venv/Scripts/python.exe project.py "FastAPI-приложение для заметок с SQLite" --out myapp [--code deepseek|lmstudio|local] | |
| """ | |
| import json | |
| import os | |
| import re | |
| import subprocess | |
| import sys | |
| from brain import pick, generate | |
| def parse_json(text): | |
| m = re.search(r"\{.*\}", text, re.DOTALL) | |
| if not m: | |
| return None | |
| try: | |
| d = json.loads(m.group(0)) | |
| return d if isinstance(d, dict) else None | |
| except Exception: | |
| return None | |
| def run_project_tests(path): | |
| try: | |
| r = subprocess.run([sys.executable, "-m", "unittest", "discover", "-s", path], | |
| capture_output=True, text=True, timeout=120) | |
| return r.returncode == 0, (r.stdout or "") + (r.stderr or "") | |
| except Exception as e: | |
| return False, f"тесты: {e}" | |
| def main(): | |
| args = sys.argv[1:] | |
| outdir = "generated_project" | |
| backend = None | |
| parts = [] | |
| i = 0 | |
| while i < len(args): | |
| if args[i] == "--out" and i + 1 < len(args): | |
| outdir = args[i + 1]; i += 2; continue | |
| if args[i] == "--code" and i + 1 < len(args): | |
| backend = args[i + 1]; i += 2; continue | |
| parts.append(args[i]); i += 1 | |
| prompt = " ".join(parts) or "Консольный TODO-менеджер с сохранением в JSON" | |
| backend = pick(backend) | |
| msgs = [{"role": "system", | |
| "content": "Ты — senior-разработчик. Проектируй и пиши рабочий код без заглушек."}, | |
| {"role": "user", | |
| "content": f"Сгенерируй проект по ТЗ: {prompt}\n\nВерни СТРОГО JSON: " | |
| f"{{\"files\": {{\"<имя.py>\": \"<полный код>\", ...}}}}. Включи " | |
| f"основной модуль, вспомогательные модули (при нужде) и файл " | |
| f"test_*.py с unittest-тестами. Без пояснений вне JSON."}] | |
| data = None | |
| for _ in range(3): | |
| raw = generate(backend, msgs, 3072) | |
| data = parse_json(raw) | |
| if data and data.get("files"): | |
| break | |
| if not data or not data.get("files"): | |
| print("[!] не удалось получить валидный JSON-проект. Попробуй ещё раз.") | |
| sys.exit(1) | |
| os.makedirs(outdir, exist_ok=True) | |
| for name, content in data["files"].items(): | |
| safe = os.path.basename(name) # защита от путей вроде ../../x | |
| with open(os.path.join(outdir, safe), "w", encoding="utf-8") as f: | |
| f.write(content) | |
| print(f"[*] проект создан в {outdir}: {len(data['files'])} файлов") | |
| ok, out = run_project_tests(outdir) | |
| if ok: | |
| print("[OK] тесты проекта прошли") | |
| else: | |
| print(f"[!] тесты падают:\n{out[-600:]}") | |
| if __name__ == "__main__": | |
| main() | |