Instructions to use zenlm/zen-guard-gen-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zenlm/zen-guard-gen-8b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="zenlm/zen-guard-gen-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-guard-gen-8b")
model = AutoModelForCausalLM.from_pretrained("zenlm/zen-guard-gen-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use zenlm/zen-guard-gen-8b with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("zenlm/zen-guard-gen-8b")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use zenlm/zen-guard-gen-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zenlm/zen-guard-gen-8b"
# 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-guard-gen-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zenlm/zen-guard-gen-8b

SGLang

How to use zenlm/zen-guard-gen-8b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "zenlm/zen-guard-gen-8b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zenlm/zen-guard-gen-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "zenlm/zen-guard-gen-8b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zenlm/zen-guard-gen-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

How to use zenlm/zen-guard-gen-8b with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "zenlm/zen-guard-gen-8b"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "zenlm/zen-guard-gen-8b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zenlm/zen-guard-gen-8b with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "zenlm/zen-guard-gen-8b"

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-guard-gen-8b

Run Hermes

hermes

MLX LM

How to use zenlm/zen-guard-gen-8b with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "zenlm/zen-guard-gen-8b"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "zenlm/zen-guard-gen-8b"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "zenlm/zen-guard-gen-8b",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use zenlm/zen-guard-gen-8b with Docker Model Runner:
```
docker model run hf.co/zenlm/zen-guard-gen-8b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Zen Guard Gen v1.0.1

Available Formats

This model is available in multiple formats for different platforms:

SafeTensors (Base Format)

Standard HuggingFace format
Compatible with Transformers library
Use for training and fine-tuning

MLX Format (Apple Silicon Optimized)

/mlx/ - Full precision MLX format
/mlx-4bit/ - 4-bit quantized (fastest on Mac)

GGUF Format (Coming Soon)

Will be added for llama.cpp compatibility
CPU-optimized for all platforms

Quick Start

Using Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("zenlm/zen-guard-gen-8b")
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-guard-gen-8b")

Using MLX (Apple Silicon)

from mlx_lm import load, generate

# Load 4-bit model (fastest)
model, tokenizer = load("zenlm/zen-guard-gen-8b", adapter_path="mlx-4bit")

# Generate
response = generate(model, tokenizer, prompt="Your prompt", max_tokens=256)
print(response)

Using llama.cpp (GGUF - Coming Soon)

llama-cli -m gguf/zen-guard-gen-q4_k_m.gguf -p "Your prompt"

Training with Zoo-Gym

pip install zoo-gym
zoo-gym train --model zenlm/zen-guard-gen-8b --data your_data.jsonl

Model Details

Architecture: Zen Guard architecture
Training: Zoo-Gym with RAIS (Recursive AI Self-Improvement System)
License: Apache 2.0
Partnership: Hanzo AI x Zoo Labs Foundation

Citation

@misc{zen_zen_guard_gen_2025,
    title={Zen Guard Gen v1.0.1},
    author={Hanzo AI and Zoo Labs Foundation},
    year={2025},
    version={1.0.1}
}

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Safetensors

Model size

8B params

Tensor type

BF16

MLX

Hardware compatibility

Quantized

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Quantizations

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