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/rag.py from Tyesee/ZuperAI-14B: direct link, hf CLI and curl.
- Browser
- Download file 3.02 kB
-
https://huggingface.co/Tyesee/ZuperAI-14B/resolve/main/toolkit/rag.py
- Command line
-
hf download hf://Tyesee/ZuperAI-14B/toolkit/rag.py
-
curl -L -o rag.py https://huggingface.co/Tyesee/ZuperAI-14B/resolve/main/toolkit/rag.py
3.02 kB
| # -*- coding: utf-8 -*- | |
| """RAG по своей кодовой базе (№1): индексация + поиск + ответ по проекту. | |
| Запуск: | |
| python rag.py index <папка> — проиндексировать проект | |
| python rag.py ask "где у меня логика авторизации?" [backend] | |
| Работает без тяжёлых зависимостей (пересечение слов). Для лучшего качества можно | |
| поставить sentence-transformers и заменить search() на эмбеддинги с косинусной близостью. | |
| """ | |
| import json | |
| import os | |
| import re | |
| import sys | |
| from brain import pick, generate | |
| INDEX = "rag_index.json" | |
| SKIP = {"venv", ".venv", ".git", "__pycache__", "node_modules", ".idea"} | |
| def chunk_file(path): | |
| code = open(path, encoding="utf-8").read() | |
| lines = code.splitlines() | |
| chunks = [] | |
| for i in range(0, len(lines), 50): | |
| chunks.append((path, i + 1, "\n".join(lines[i:i + 50]))) | |
| return chunks | |
| def index(folder): | |
| chunks = [] | |
| for root, dirs, files in os.walk(folder): | |
| dirs[:] = [d for d in dirs if d not in SKIP] | |
| for f in files: | |
| if f.endswith(".py"): | |
| chunks.extend(chunk_file(os.path.join(root, f))) | |
| json.dump(chunks, open(INDEX, "w", encoding="utf-8"), ensure_ascii=False) | |
| print(f"проиндексировано чанков: {len(chunks)} -> {INDEX}") | |
| def search(query, top=5): | |
| if not os.path.exists(INDEX): | |
| print(f"сначала проиндексируй: rag.py index <папка>") | |
| return [] | |
| chunks = json.load(open(INDEX, encoding="utf-8")) | |
| qwords = set(re.findall(r"\w+", query.lower())) | |
| scored = [] | |
| for path, line, text in chunks: | |
| twords = set(re.findall(r"\w+", text.lower())) | |
| s = len(qwords & twords) | |
| if s: | |
| scored.append((s, path, line, text)) | |
| scored.sort(reverse=True) | |
| return scored[:top] | |
| def ask(query, backend): | |
| hits = search(query) | |
| if not hits: | |
| print("ничего релевантного не найдено") | |
| return | |
| context = "\n\n".join(f"--- {p}:{l} ---\n{t[:800]}" for _, p, l, t in hits) | |
| msgs = [{"role": "system", | |
| "content": "Ты анализируешь кодовую базу. Отвечай по предоставленному " | |
| "контексту, указывай файлы и строки."}, | |
| {"role": "user", | |
| "content": f"Контекст из кодовой базы:\n{context}\n\nВопрос: {query}"}] | |
| print(generate(backend, msgs, 1024)) | |
| def main(): | |
| args = sys.argv[1:] | |
| if len(args) < 2: | |
| print("usage: rag.py index <папка> | rag.py ask <вопрос> [backend]") | |
| sys.exit(2) | |
| if args[0] == "index": | |
| index(args[1]) | |
| elif args[0] == "ask": | |
| ask(" ".join(args[1:]), pick()) | |
| if __name__ == "__main__": | |
| main() | |