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 Feb 28

Commit

4142591

verified ·

1 Parent(s): 5e58ccb

Add config.json for inference API compatibility

Browse files

Files changed (1) hide show

config.json +30 -0

config.json ADDED Viewed

	@@ -0,0 +1,30 @@

+{
+  "architectures": [
+    "Qwen3VLForConditionalGeneration"
+  ],
+  "model_type": "designer-instruct",
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.44.2",
+  "thinking_tokens": 512000,
+  "_name_or_path": "zenlm/zen-designer-235b-a22b-instruct",
+  "_base_model": "zenlm/zen-designer-235b-a22b-instruct",
+  "vision_config": {
+    "hidden_size": 2048,
+    "image_size": 2048,
+    "num_hidden_layers": 48,
+    "patch_size": 14
+  },
+  "text_config": {
+    "vocab_size": 151936,
+    "hidden_size": 8192,
+    "num_hidden_layers": 80,
+    "num_attention_heads": 64,
+    "num_key_value_heads": 8,
+    "max_position_embeddings": 131072
+  },
+  "num_experts": 64,
+  "num_experts_per_tok": 4,
+  "expert_interval": 1,
+  "_total_params": "235B",
+  "_active_params": "22B"
+}