zen-designer-gguf

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

Libraries

How to use zenlm/zen-designer-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="zenlm/zen-designer-gguf",
	filename="GGUF/Q4_K_M/Q4_K_M-00001-of-00003.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/zen-designer-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/zen-designer-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zenlm/zen-designer-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/zen-designer-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zenlm/zen-designer-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-designer-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf zenlm/zen-designer-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-designer-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf zenlm/zen-designer-gguf:Q4_K_M

Use Docker

docker model run hf.co/zenlm/zen-designer-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use zenlm/zen-designer-gguf with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/zenlm/zen-designer-gguf:Q4_K_M

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

Unsloth Studio

How to use zenlm/zen-designer-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-designer-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-designer-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-designer-gguf to start chatting

How to use zenlm/zen-designer-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/zen-designer-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-designer-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zenlm/zen-designer-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/zen-designer-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-designer-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

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

Lemonade

How to use zenlm/zen-designer-gguf with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.zen-designer-gguf-Q4_K_M

List all available models

lemonade list

Zen Designer GGUF: 235B Vision-Language Model (Abliterated)

235B MoE | Vision-Language | GGUF Quantized | Abliterated

Fine-tuned from Qwen3-VL-235B-A22B-Instruct (Apache-2.0) with Hanzo identity + agentic-data training + abliteration, then GGUF-quantized. A 235B-total / 22B-active Mixture-of-Experts vision-language model supporting images, video, documents, charts, GUIs, and spatial reasoning with 256K context.

Model Specifications

Attribute	Value
Base Model	Qwen3-VL-235B-A22B-Instruct (Apache-2.0)
Parameters	235B total / 22B active (MoE)
Architecture	Vision-language transformer (Mixture of Experts)
Context Window	256K tokens
Modalities	Text, Images, Video, Documents
OCR Languages	32 scripts
License	Apache 2.0

Available Formats

Format	Size	Description	Recommended Use
Q2_K (split)	~60 GB	2-bit quantization, 15-part split	Servers with 64+ GB RAM, maximum scale
Q4_K_M	~142 GB	4-bit quantization, single or split	Best quality/size tradeoff for local inference

Quick Start

llama.cpp

# Download a split (Q2_K example — replace with Q4_K_M filename as appropriate)
# Then run:
llama-cli \
  --model zen-designer-235b-a22b-instruct-abliterated-Q2_K-00001-of-00015.gguf \
  --mmproj mmproj-zen-designer-235b-a22b-instruct-abliterated-f16.gguf \
  --image your_image.jpg \
  --prompt "Describe this image in detail." \
  -n 1024 \
  --ctx-size 8192 \
  --temp 0.7

For multi-part files, place all split parts in the same directory and point --model to part 00001.

Vision Tasks

Zen Designer handles a broad range of visual inputs:

Image analysis and description
Document and PDF parsing
Chart and table extraction
GUI navigation and screen understanding
Video understanding with temporal reasoning
Bounding box and spatial grounding

Abliteration

This model has been abliterated — a technique that removes refusal behaviors encoded in the model weights without fine-tuning. The process works by identifying the refusal direction in the model's residual stream and projecting it out of the weight matrices.

What abliteration does:

Removes hardcoded refusal responses
Preserves all other capabilities and knowledge
Does not alter factual knowledge or reasoning ability

What abliteration does not do:

Add harmful knowledge the base model lacked
Guarantee any specific behavior
Replace a system prompt or application-level safety policy

Users are responsible for appropriate deployment and use of abliterated models. Apply system prompts and application-layer controls to define model behavior for your use case.

Attribution

Built on Qwen3-VL-235B-A22B-Instruct by the Qwen team, Alibaba Group, released under the Apache License 2.0. Hanzo's contribution is identity training, agentic-data fine-tuning, and abliteration on top of that base, distributed here in GGUF format. The base model is used under the terms of the Apache License, Version 2.0.

Model Family

Model	Format	Parameters	Context
zen-designer-235b-a22b-instruct	SafeTensors	235B / 22B active	256K
zen-designer-gguf	GGUF	235B / 22B active	256K

Model tree for zenlm/zen-designer-gguf

Base model

Qwen/Qwen3-VL-235B-A22B-Instruct

Quantized

(28)

this model

Collection including zenlm/zen-designer-gguf

Zen Specialty

Collection

Vertical-specific finetunes — finance, medical, legal, sql, translate, scribe, designer, etc. • 15 items • Updated 12 days ago

Zen Designer GGUF: 235B Vision-Language Model (Abliterated)

Model Specifications

Available Formats

Quick Start

llama.cpp

Vision Tasks

Abliteration

Attribution

Model Family

Links

Model tree for zenlm/zen-designer-gguf

Collection including zenlm/zen-designer-gguf