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/code_utils.py from Tyesee/ZuperAI-14B: direct link, hf CLI and curl.
- Browser
- Download file 2.34 kB
-
https://huggingface.co/Tyesee/ZuperAI-14B/resolve/main/toolkit/code_utils.py
- Command line
-
hf download hf://Tyesee/ZuperAI-14B/toolkit/code_utils.py
-
curl -L -o code_utils.py https://huggingface.co/Tyesee/ZuperAI-14B/resolve/main/toolkit/code_utils.py
2.34 kB
| # -*- coding: utf-8 -*- | |
| """Преобразования кода — три режима в одном инструменте. | |
| Запуск: | |
| python code_utils.py docs <файл.py> — докстринги + README (№3) | |
| python code_utils.py refactor <файл.py> — рефакторинг/чистка (№4) | |
| python code_utils.py translate <файл.py> <язык> — перевод кода (№5) | |
| Языки перевода: python, javascript, typescript, go, rust, java, cpp, csharp. | |
| """ | |
| import sys | |
| from brain import pick, generate | |
| def main(): | |
| args = sys.argv[1:] | |
| if len(args) < 2: | |
| print("usage: code_utils.py <docs|refactor|translate> <файл> [язык]") | |
| sys.exit(2) | |
| mode, path = args[0], args[1] | |
| backend = pick() | |
| try: | |
| code = open(path, encoding="utf-8").read() | |
| except Exception as e: | |
| print(f"не прочитан: {e}") | |
| sys.exit(1) | |
| if mode == "docs": | |
| prompt = (f"Сгенерируй документацию для этого кода: добавь докстринги и " | |
| f"комментарии на русском, затем короткий README-блок (что делает, " | |
| f"как запустить). Верни код с докстрингами, потом README.\n\n{code}") | |
| elif mode == "refactor": | |
| prompt = (f"Отрефактори этот код: понятные имена, упрощение, выделение " | |
| f"повторяющегося в функции, чистота. Поведение сохрани. Верни ТОЛЬКО " | |
| f"исправленный код, без пояснений и без ```.\n\n{code}") | |
| elif mode == "translate": | |
| lang = args[2] if len(args) > 2 else "javascript" | |
| prompt = (f"Переведи этот код на {lang}, сохранив логику и структуру. Верни " | |
| f"ТОЛЬКО переведённый код, без пояснений и без ```.\n\n{code}") | |
| else: | |
| print("неизвестный режим: docs | refactor | translate") | |
| sys.exit(2) | |
| print(generate(backend, [{"role": "user", "content": prompt}], 2048)) | |
| if __name__ == "__main__": | |
| main() | |