zen-designer-gguf

Instructions to use zenlm/zen-designer-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/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

zeekay commited on 19 days ago

Commit

cbcbde2

verified ·

1 Parent(s): 0424542

docs: honest attribution — base_model Qwen3-VL-235B-A22B-Instruct + upstream credit

Browse files

Files changed (1) hide show

README.md +11 -2

README.md CHANGED Viewed

@@ -11,6 +11,8 @@ language:
 - it
 - ru
 license: apache-2.0
 tags:
 - text-generation
 - vision
@@ -22,7 +24,7 @@ tags:
 - moe
 - ocr
 - document-understanding
-  - hanzo
 pipeline_tag: text-generation
 library_name: gguf
 ---
@@ -31,7 +33,7 @@ library_name: gguf
 **235B MoE | Vision-Language | GGUF Quantized | Abliterated**
-GGUF quantized and abliterated version of [Zen Designer](https://huggingface.co/zenlm/zen-designer-235b-a22b-instruct) — the 235B flagship vision-language model from Zen LM. Supports images, video, documents, charts, GUIs, and spatial reasoning with 256K context.
 ---
@@ -39,6 +41,7 @@ GGUF quantized and abliterated version of [Zen Designer](https://huggingface.co/
 | Attribute | Value |
 |-----------|-------|
 | Parameters | 235B total / 22B active (MoE) |
 | Architecture | Vision-language transformer (Mixture of Experts) |
 | Context Window | 256K tokens |
@@ -106,6 +109,12 @@ Users are responsible for appropriate deployment and use of abliterated models.
 ---
 ## Model Family
 | Model | Format | Parameters | Context |

 - it
 - ru
 license: apache-2.0
+base_model:
+- Qwen/Qwen3-VL-235B-A22B-Instruct
 tags:
 - text-generation
 - vision
 - moe
 - ocr
 - document-understanding
+- hanzo
 pipeline_tag: text-generation
 library_name: gguf
 ---
 **235B MoE | Vision-Language | GGUF Quantized | Abliterated**
+Fine-tuned from [Qwen3-VL-235B-A22B-Instruct](https://huggingface.co/Qwen/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.
 ---
 | Attribute | Value |
 |-----------|-------|
+| Base Model | [Qwen3-VL-235B-A22B-Instruct](https://huggingface.co/Qwen/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 |
 ---
+## Attribution
+Built on [Qwen3-VL-235B-A22B-Instruct](https://huggingface.co/Qwen/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 |