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/selfcheck.py from Tyesee/ZuperAI-14B: direct link, hf CLI and curl.
- Browser
- Download file 2.55 kB
-
https://huggingface.co/Tyesee/ZuperAI-14B/resolve/main/toolkit/selfcheck.py
- Command line
-
hf download hf://Tyesee/ZuperAI-14B/toolkit/selfcheck.py
-
curl -L -o selfcheck.py https://huggingface.co/Tyesee/ZuperAI-14B/resolve/main/toolkit/selfcheck.py
2.55 kB
| # -*- coding: utf-8 -*- | |
| """Самопроверка сгенерированного кода (безопасно, без исполнения). | |
| Возвращает (ok, message). Ловит синтаксические ошибки и неопределённые имена. | |
| """ | |
| import ast | |
| import io | |
| def check_python(code): | |
| """Проверяет Python-код. Безопасно — ничего не исполняет.""" | |
| if not code or not code.strip(): | |
| return False, "пустой код" | |
| # 1) синтаксис (ast) | |
| try: | |
| ast.parse(code) | |
| except SyntaxError as e: | |
| return False, f"SyntaxError: {e.msg} (строка {e.lineno})" | |
| # 2) компиляция в байткод (ловит чуть больше, всё ещё без исполнения) | |
| try: | |
| compile(code, "<generated>", "exec") | |
| except Exception as e: | |
| return False, f"CompileError: {type(e).__name__}: {e}" | |
| # 3) pyflakes — только неопределённые имена (типовые опечатки/пропущенные импорты) | |
| try: | |
| from pyflakes.api import check as _pf | |
| from pyflakes.reporter import Reporter | |
| buf = io.StringIO() | |
| _pf(code, "<generated>", Reporter(buf, buf)) | |
| undef = [ln.strip() for ln in buf.getvalue().splitlines() | |
| if "undefined name" in ln] | |
| if undef: | |
| return False, "Неопределённые имена: " + "; ".join(undef)[:400] | |
| except ImportError: | |
| pass | |
| except Exception: | |
| pass | |
| return True, "ок" | |
| def check_js(code): | |
| """Проверяет JS/TS синтаксис через node --check (если node установлен).""" | |
| import subprocess | |
| import tempfile | |
| import os | |
| if not code.strip(): | |
| return False, "пустой код" | |
| fd, path = tempfile.mkstemp(suffix=".js") | |
| try: | |
| with os.fdopen(fd, "w", encoding="utf-8") as f: | |
| f.write(code) | |
| r = subprocess.run(["node", "--check", path], capture_output=True, text=True, | |
| timeout=30) | |
| if r.returncode != 0: | |
| return False, "JS: " + (r.stderr or r.stdout).strip()[:300] | |
| return True, "ок" | |
| except FileNotFoundError: | |
| return True, "ок (node не установлен — проверка пропущена)" | |
| except Exception as e: | |
| return False, f"JS check: {e}" | |
| finally: | |
| try: | |
| os.remove(path) | |
| except OSError: | |
| pass | |