Instructions to use zenlm/zen5-mini-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use zenlm/zen5-mini-gguf 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 zenlm/zen5-mini-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zenlm/zen5-mini-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf zenlm/zen5-mini-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zenlm/zen5-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/zen5-mini-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf zenlm/zen5-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/zen5-mini-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf zenlm/zen5-mini-gguf:Q4_K_M

Use Docker

docker model run hf.co/zenlm/zen5-mini-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use zenlm/zen5-mini-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zenlm/zen5-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/zen5-mini-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zenlm/zen5-mini-gguf:Q4_K_M

Ollama
How to use zenlm/zen5-mini-gguf with Ollama:
```
ollama run hf.co/zenlm/zen5-mini-gguf:Q4_K_M
```

Unsloth Studio

How to use zenlm/zen5-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/zen5-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/zen5-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/zen5-mini-gguf to start chatting

How to use zenlm/zen5-mini-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zenlm/zen5-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/zen5-mini-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use zenlm/zen5-mini-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zenlm/zen5-mini-gguf: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 "zenlm/zen5-mini-gguf: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"

Docker Model Runner
How to use zenlm/zen5-mini-gguf with Docker Model Runner:
```
docker model run hf.co/zenlm/zen5-mini-gguf:Q4_K_M
```

Lemonade

How to use zenlm/zen5-mini-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull zenlm/zen5-mini-gguf:Q4_K_M

Run and chat with the model

lemonade run user.zen5-mini-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use zenlm/zen5-mini-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zenlm/zen5-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/zen5-mini-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

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.

Repackaged from MiniMaxAI/MiniMax-M2.5 (modified-MIT, MiniMax) — quantized to GGUF from the abliterated variant by huihui-ai. Not trained from scratch — a permissively-licensed redistribution for the OSS-clean Zen model line.

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)	← 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

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?"

License

Upstream: MiniMaxAI/MiniMax-M2.5 by MiniMax, licensed modified-MIT (see the upstream LICENSE); abliterated variant by huihui-ai. This repository redistributes a quantized derivative; the upstream license terms are retained.

Downloads last month: 99

GGUF

Model size

229B params

Architecture

minimax-m2

Hardware compatibility

4-bit

Model tree for zenlm/zen5-mini-gguf

Base model

MiniMaxAI/MiniMax-M2.5

Quantized

(63)

this model

Collection including zenlm/zen5-mini-gguf

Zen5 Chat Ladder

Collection

Canonical Zen5 lineup, smallest to largest. • 6 items • Updated May 30