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/brain.py from Tyesee/ZuperAI-14B: direct link, hf CLI and curl.
- Browser
- Download file 1.84 kB
-
https://huggingface.co/Tyesee/ZuperAI-14B/resolve/main/toolkit/brain.py
- Command line
-
hf download hf://Tyesee/ZuperAI-14B/toolkit/brain.py
-
curl -L -o brain.py https://huggingface.co/Tyesee/ZuperAI-14B/resolve/main/toolkit/brain.py
1.84 kB
| # -*- coding: utf-8 -*- | |
| """Общий модуль генерации: единая точка доступа к «мозгам». | |
| Мозги: deepseek (API), lmstudio (локальная GGUF через LM Studio), local (transformers+LoRA 3B). | |
| Все остальные инструменты (chat, project, docs, refactor, translate, rag, api) используют это. | |
| """ | |
| from webui import (deepseek_available, deepseek_generate, | |
| lmstudio_available, lmstudio_generate, _strip_fences) | |
| def available(): | |
| return {"deepseek": deepseek_available(), "lmstudio": lmstudio_available()} | |
| def pick(backend=None): | |
| """Выбирает мозг: явный, иначе лучший доступный (lmstudio 14B → deepseek → local 3B). | |
| Локальная 14B (LM Studio) — главный мозг: офлайн и приватно. | |
| DeepSeek — фолбэк/судья для самопроверки, local 3B — крайний случай. | |
| """ | |
| if backend in ("deepseek", "lmstudio", "local"): | |
| return backend | |
| a = available() | |
| if a["lmstudio"]: | |
| return "lmstudio" | |
| if a["deepseek"]: | |
| return "deepseek" | |
| return "local" | |
| def generate(backend, messages, max_tokens=1024): | |
| """Генерация текста заданным мозгом. Возвращает очищенный от ```-обёрток текст.""" | |
| if backend == "deepseek": | |
| return _strip_fences(deepseek_generate(messages, max_tokens=max_tokens)) | |
| if backend == "lmstudio": | |
| return _strip_fences(lmstudio_generate(messages, max_tokens=max_tokens)) | |
| from infer_smart import load, gen | |
| if not hasattr(generate, "_m"): | |
| generate._m = load() | |
| tok, model = generate._m | |
| return _strip_fences(gen(tok, model, messages, max_tokens=max_tokens)) | |