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/fix_project.py from Tyesee/ZuperAI-14B: direct link, hf CLI and curl.
- Browser
- Download file 5.25 kB
-
https://huggingface.co/Tyesee/ZuperAI-14B/resolve/main/toolkit/fix_project.py
- Command line
-
hf download hf://Tyesee/ZuperAI-14B/toolkit/fix_project.py
-
curl -L -o fix_project.py https://huggingface.co/Tyesee/ZuperAI-14B/resolve/main/toolkit/fix_project.py
5.25 kB
| # -*- coding: utf-8 -*- | |
| """ZuperAI — агент «почини мой проект». | |
| Сканирует папку на .py-файлы, прогоняет статический анализ (синтаксис + undefined | |
| names), а при наличии ошибок — чинит каждый файл через модель, затем перепроверяет. | |
| Опционально запускает тесты (pytest/unittest), если они есть в проекте. | |
| Запуск: | |
| venv/Scripts/python.exe fix_project.py <путь_к_проекту> [--code deepseek|lmstudio|local] | |
| Ключи: | |
| --code кто чинит код (по умолчанию deepseek, если ключ есть) | |
| --tests запускать ли тесты после статического анализа (по умолчанию нет) | |
| --dry не перезаписывать файлы, только показать найденные ошибки | |
| """ | |
| import os | |
| import subprocess | |
| import sys | |
| from selfcheck import check_python | |
| from webui import deepseek_available, deepseek_generate, lmstudio_generate, _strip_fences | |
| SKIP_DIRS = {"venv", ".venv", "__pycache__", ".git", "node_modules", ".idea"} | |
| def scan_py(path): | |
| out = [] | |
| for root, dirs, files in os.walk(path): | |
| dirs[:] = [d for d in dirs if d not in SKIP_DIRS] | |
| for f in files: | |
| if f.endswith(".py"): | |
| out.append(os.path.join(root, f)) | |
| return sorted(out) | |
| def _gen(backend, msgs, max_tokens): | |
| if backend == "deepseek": | |
| return _strip_fences(deepseek_generate(msgs, max_tokens=max_tokens)) | |
| if backend == "lmstudio": | |
| return _strip_fences(lmstudio_generate(msgs, max_tokens=max_tokens)) | |
| from infer_smart import load, gen | |
| if not hasattr(_gen, "_m"): | |
| _gen._m = load() | |
| tok, model = _gen._m | |
| return _strip_fences(gen(tok, model, msgs, max_tokens=max_tokens)) | |
| def fix_code(code, error, backend): | |
| msgs = [{"role": "system", | |
| "content": "Ты — senior-разработчик. Исправляй код по описанию ошибки. " | |
| "Верни ТОЛЬКО полный исправленный файл, без пояснений и без ```."}, | |
| {"role": "user", | |
| "content": f"Вот код:\n{code}\n\nОшибка: {error}\n\nИсправь код."}] | |
| return _gen(backend, msgs, 2048) | |
| def run_tests(path): | |
| """Пробует запустить тесты проекта, возвращает (ok, output).""" | |
| try: | |
| r = subprocess.run([sys.executable, "-m", "pytest", "-q"], cwd=path, | |
| capture_output=True, text=True, timeout=120) | |
| return r.returncode == 0, (r.stdout or "") + (r.stderr or "") | |
| except subprocess.TimeoutExpired: | |
| return False, "тесты зависли (таймаут 120с)" | |
| except Exception as e: | |
| # pytest может не быть установлен — пробуем unittest | |
| 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 e2: | |
| return None, f"тесты не найдены/не запустились: {e2}" | |
| def main(): | |
| args = sys.argv[1:] | |
| if not args: | |
| print("usage: fix_project.py <путь> [--code deepseek|lmstudio|local] [--tests] [--dry]") | |
| sys.exit(2) | |
| path = args[0] | |
| backend = "deepseek" if deepseek_available() else "local" | |
| run_tests_flag = False | |
| dry = False | |
| for i, a in enumerate(args[1:], 1): | |
| if a == "--code" and i + 1 < len(args): | |
| backend = args[i + 1] | |
| elif a == "--tests": | |
| run_tests_flag = True | |
| elif a == "--dry": | |
| dry = True | |
| files = scan_py(path) | |
| print(f"найдено .py файлов: {len(files)}") | |
| fixed_any = False | |
| for f in files: | |
| try: | |
| code = open(f, encoding="utf-8").read() | |
| except Exception as e: | |
| print(f"! {f}: не прочитан ({e})") | |
| continue | |
| ok, msg = check_python(code) | |
| if ok: | |
| continue | |
| print(f"[!] {f}: {msg}") | |
| fixed_any = True | |
| if dry: | |
| continue | |
| fixed = fix_code(code, msg, backend) | |
| if fixed and fixed.strip() != code.strip(): | |
| ok2, msg2 = check_python(fixed) | |
| if ok2: | |
| open(f, "w", encoding="utf-8").write(fixed) | |
| print(f" -> исправлено") | |
| else: | |
| print(f" -> модель не починила: {msg2}") | |
| if run_tests_flag and not dry: | |
| print("--- запуск тестов ---") | |
| ok, out = run_tests(path) | |
| if ok is None: | |
| print(out) | |
| elif ok: | |
| print("[OK] тесты прошли") | |
| else: | |
| print(f"[!] тесты падают:\n{out[-800:]}") | |
| if not fixed_any: | |
| print("чисто — статических ошибок не найдено") | |
| if __name__ == "__main__": | |
| main() | |