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

Libraries

How to use zenlm/zen5-pro-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="zenlm/zen5-pro-gguf",
	filename="DeepSeek-V4-Flash-IQ2XXS-w2Q2K-AProjQ8-SExpQ8-OutQ8-chat-v2-imatrix.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use zenlm/zen5-pro-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-pro-gguf:F32
# Run inference directly in the terminal:
llama cli -hf zenlm/zen5-pro-gguf:F32

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf zenlm/zen5-pro-gguf:F32
# Run inference directly in the terminal:
llama cli -hf zenlm/zen5-pro-gguf:F32

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-pro-gguf:F32
# Run inference directly in the terminal:
./llama-cli -hf zenlm/zen5-pro-gguf:F32

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-pro-gguf:F32
# Run inference directly in the terminal:
./build/bin/llama-cli -hf zenlm/zen5-pro-gguf:F32

Use Docker

docker model run hf.co/zenlm/zen5-pro-gguf:F32

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

docker model run hf.co/zenlm/zen5-pro-gguf:F32

Ollama
How to use zenlm/zen5-pro-gguf with Ollama:
```
ollama run hf.co/zenlm/zen5-pro-gguf:F32
```

Unsloth Studio

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

How to use zenlm/zen5-pro-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-pro-gguf:F32

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-pro-gguf:F32"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zenlm/zen5-pro-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-pro-gguf:F32

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-pro-gguf:F32

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use zenlm/zen5-pro-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-pro-gguf:F32

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-pro-gguf:F32" \
  --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-pro-gguf with Docker Model Runner:
```
docker model run hf.co/zenlm/zen5-pro-gguf:F32
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull zenlm/zen5-pro-gguf:F32

Run and chat with the model

lemonade run user.zen5-pro-gguf-F32

List all available models

lemonade list

Zen5 Pro

High-quality Zen5 tier that fits on a single 128 GB unified-memory machine. Sparse MoE with 284B total / 37B active parameters per token, 1M context, asymmetric routed-MoE quantization (routed IQ2_XXS up/gate, Q2_K down; shared experts, attention projections, routing logits and the LM head left at higher precision).

Repackaged from deepseek-ai/DeepSeek-V4-Flash (mit, DeepSeek) — 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.

Runs on a single 128 GB Apple Silicon (M3/M4 Max), DGX Spark (GB10), or H100 80 GB with the zen5-engine.

Part of the canonical Zen5 ladder:

SKU	Hardware fit	This repo
`zen5-flash`	anything	zen-5-flash-gguf
`zen5-mini`	32 GB	zen-5-mini-gguf
`zen5` (default)	24 GB+ VRAM	zen-5-gguf
`zen5-pro`	128 GB single-machine	← you are here
`zen5-max`	512 GB / 8x H100	zen-5-max-gguf

Files

File pattern	Size	Notes
`-IQ2XXS-w2Q2K--chat-v2-imatrix.gguf`	81 GB	Recommended — IQ2_XXS routed-MoE with imatrix calibration, fits 128 GB
`-Layers37-42Q4KExperts--imatrix-fixed.gguf`	91 GB	Mixed-quant — higher quality at the boundary layers, fits 128 GB
`-Q4KExperts-F16HC--imatrix.gguf`	153 GB	Q4-imatrix — for 256 GB+ unified-memory machines
`*-MTP-Q4K-Q8_0-F32.gguf`	3.5 GB	Optional speculative-decoding draft heads (use with `--mtp`)
`-IQ2XXS-w2Q2K--chat-v2.gguf` (non-imatrix)	81 GB	Legacy Q2 — prefer the `-imatrix` version above
`-Q4KExperts-F16HC--chat-v2.gguf` (non-imatrix)	153 GB	Legacy Q4 — prefer the `-imatrix` version above

Run

Hosted via the Hanzo gateway (api.hanzo.ai) as zen5-pro.

Local with the zen5-engine:

git clone https://github.com/zenlm/zen5-engine
cd zen5-engine && make                  # macOS Metal
                       # or: make cuda-spark    # DGX Spark GB10
                       # or: make cuda-generic  # generic CUDA box

./download_model.sh q2-imatrix          # pulls this repo's recommended GGUF
./zen5 -p "Hello"
./zen5-server --ctx 100000 --kv-disk-dir /tmp/zen5-kv --kv-disk-space-mb 8192

Performance (Metal, `--ctx 32768 --nothink`)

Machine	Prompt	Prefill	Generation
MacBook Pro M3 Max 128 GB	short	58.5 t/s	26.7 t/s
MacBook Pro M3 Max 128 GB	11709 tok	250.1 t/s	21.5 t/s
Mac Studio M3 Ultra 512 GB	short	84.4 t/s	36.9 t/s
Mac Studio M3 Ultra 512 GB	12018 tok (Q4)	448.8 t/s	26.6 t/s
DGX Spark GB10 128 GB	7047 tok	343.8 t/s	13.7 t/s

License

apache-2.0 (this packaging). Upstream: deepseek-ai/DeepSeek-V4-Flash by DeepSeek, licensed MIT; abliterated variant by huihui-ai. This repository redistributes a quantized derivative; the upstream MIT terms are retained.

Downloads last month: 730

GGUF

Model size

284B params

Architecture

deepseek4

Hardware compatibility

16-bit

32-bit

View +2 variants

Model tree for zenlm/zen5-pro-gguf

Base model

deepseek-ai/DeepSeek-V4-Flash

Quantized

(103)

this model

Collection including zenlm/zen5-pro-gguf

Zen5 Chat Ladder

Collection

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

Zen5 Pro

Files

Run

Performance (Metal, --ctx 32768 --nothink)

License

Model tree for zenlm/zen5-pro-gguf

Collection including zenlm/zen5-pro-gguf

Performance (Metal, `--ctx 32768 --nothink`)