Instructions to use zenlm/zen-5-mini-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use zenlm/zen-5-mini-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="zenlm/zen-5-mini-gguf", filename="MiniMax-M2.5-abliterated-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use zenlm/zen-5-mini-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf zenlm/zen-5-mini-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf zenlm/zen-5-mini-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf zenlm/zen-5-mini-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf zenlm/zen-5-mini-gguf: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 zenlm/zen-5-mini-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf zenlm/zen-5-mini-gguf: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 zenlm/zen-5-mini-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf zenlm/zen-5-mini-gguf:Q4_K_M
Use Docker
docker model run hf.co/zenlm/zen-5-mini-gguf:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use zenlm/zen-5-mini-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zenlm/zen-5-mini-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zenlm/zen-5-mini-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/zenlm/zen-5-mini-gguf:Q4_K_M
- Ollama
How to use zenlm/zen-5-mini-gguf with Ollama:
ollama run hf.co/zenlm/zen-5-mini-gguf:Q4_K_M
- Unsloth Studio new
How to use zenlm/zen-5-mini-gguf with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for zenlm/zen-5-mini-gguf to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for zenlm/zen-5-mini-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for zenlm/zen-5-mini-gguf to start chatting
- Pi new
How to use zenlm/zen-5-mini-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf zenlm/zen-5-mini-gguf:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "zenlm/zen-5-mini-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use zenlm/zen-5-mini-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf zenlm/zen-5-mini-gguf: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 zenlm/zen-5-mini-gguf:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use zenlm/zen-5-mini-gguf with Docker Model Runner:
docker model run hf.co/zenlm/zen-5-mini-gguf:Q4_K_M
- Lemonade
How to use zenlm/zen-5-mini-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull zenlm/zen-5-mini-gguf:Q4_K_M
Run and chat with the model
lemonade run user.zen-5-mini-gguf-Q4_K_M
List all available models
lemonade list
Zen5 Mini
Frontier-agentic tier of the Zen5 family at the lowest cost in the lineup. Zen agentic MoE with ~10B active parameters per token; trained on large-scale real-world environment RL for tool use, code, and multi-step reasoning.
Part of the canonical Zen5 ladder:
| SKU | Hardware fit | This repo |
|---|---|---|
zen5-flash |
anything (4 GB VRAM) | zen-5-flash-gguf |
zen5-mini |
32 GB unified RAM (Q4_K_M) | β you are here |
zen5 (default) |
24 GB+ VRAM | zen-5-gguf |
zen5-pro |
Mac M4 Max / DGX Spark / H100 80GB | zen-5-pro-gguf |
zen5-max |
Mac Studio M3 Ultra 512GB / 8x H100 | zen-5-max-gguf |
Files
| File | Size | Quant |
|---|---|---|
main GGUF (*-Q4_K_M.gguf) |
~140 GB | Q4_K_M, refusal-orthogonalized |
Run
Hosted via the Hanzo gateway (api.hanzo.ai) as zen5-mini.
Local with llama.cpp or compatible:
hf download zenlm/zen-5-mini-gguf --local-dir gguf
MAIN=$(ls gguf/*-Q4_K_M.gguf | head -1)
llama-cli -m "$MAIN" -p "Reply in one sentence: what year is it?"
Acknowledgements
Built on MiniMaxAI/MiniMax-M2.5 (Apache-2.0). Abliterated variant from jiaojjjjje (mirrored here for the Zen5 canonical distribution).
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Model tree for zenlm/zen-5-mini-gguf
Base model
MiniMaxAI/MiniMax-M2.5