zen-omni

Multimodal AI Model supporting Text, Vision, and Audio

Part of the Zen LM family - democratizing AI while protecting our planet.

Model Overview

zen-omni is a multimodal model based on Qwen3-Omni architecture, capable of processing and understanding:

• 📝 Text - Natural language understanding and generation
• 🖼️ Vision - Image analysis and visual reasoning
• 🎵 Audio - Speech recognition and audio understanding

This is a true omni-modal model with unified cross-modal reasoning capabilities.

Architecture

Base: Qwen3-Omni (Unified Multimodal Architecture)
Type: Multimodal Transformer
Parameters: ~7B
Context Length: 32,768 tokens

Components

• Text Encoder: Transformer-based language model
• Vision Encoder: Vision transformer for image understanding
• Audio Encoder: Speech transformer for audio processing
• Multimodal Fusion: Cross-attention mechanisms for unified understanding

Capabilities

✨ Cross-Modal Understanding

• Process text, images, and audio simultaneously
• Reason across different modalities
• Unified representation learning

🎯 Text Understanding

• Natural language processing
• Instruction following
• Text generation

🖼️ Vision Understanding

• Image analysis and description
• Visual question answering
• Scene understanding

🎙️ Audio Understanding

• Speech recognition
• Audio transcription
• Voice interaction

Model Variants

• zen-omni - Base multimodal model (this repository)
• zen-omni-30b-instruct - Instruction-tuned variant
• zen-omni-30b-thinking - Chain-of-thought reasoning variant

Quick Start

from transformers import AutoModelForCausalLM, AutoProcessor

# Load model and processor
model = AutoModelForCausalLM.from_pretrained("zenlm/zen-omni")
processor = AutoProcessor.from_pretrained("zenlm/zen-omni")

# Text input
text_input = processor(text="Hello!", return_tensors="pt")
output = model.generate(**text_input)

# Image + Text input (multimodal)
image_input = processor(
    text="What's in this image?",
    images=image,  # PIL Image
    return_tensors="pt"
)
output = model.generate(**image_input)

# Audio + Text input (multimodal)
audio_input = processor(
    text="What do you hear?",
    audio=audio_array,  # Audio data
    return_tensors="pt"
)
output = model.generate(**audio_input)

response = processor.decode(output[0])

Use Cases

• 🎨 Multimodal Assistants: Interact with text, images, and voice
• 📊 Visual Question Answering: Answer questions about images
• 🎙️ Voice Interfaces: Build voice-enabled applications
• 📱 Accessibility Tools: Audio description and transcription
• 🤖 Cross-Modal AI: Tasks requiring understanding multiple modalities

Training

Fine-tuned from Qwen3-Omni with:

• Multimodal instruction tuning
• Cross-modal alignment
• Audio-vision-text integration
• Zen AI identity and safety training

Technical Requirements

Important: This is a multimodal model and requires:

• Multimodal-compatible transformers library
• AutoProcessor (not just tokenizer)
• Support for image and audio inputs
• Qwen3-Omni compatible inference code

Why Zen LM?

🚀 Ultra-Efficient - Optimized for diverse hardware
🔒 Truly Private - 100% local processing, no cloud
🌱 Eco-Friendly - 95% less energy than cloud AI
💚 Free Forever - Apache 2.0 licensed

Organizations

Hanzo AI Inc - Techstars '17 • Award-winning GenAI lab • https://hanzo.ai
Zoo Labs Foundation - 501(c)(3) Non-Profit • Environmental AI • https://zoolabs.io

Links

🌐 Website: https://zenlm.org
💬 Discord: https://discord.gg/hanzoai
🐦 Twitter: https://twitter.com/hanzoai
📧 Email: [email protected]

Citation

@article{qwen3-omni,
  title={Qwen3-Omni: Unified Multimodal Understanding},
  author={Qwen Team},
  year={2024},
  url={https://github.com/QwenLM/Qwen3-Omni}
}

@software{zen-omni,
  title={Zen-Omni: Efficient Multimodal AI},
  author={Zen LM Team},
  year={2024},
  url={https://huggingface.co/zenlm/zen-omni}
}

Acknowledgments

This model is based on Qwen3-Omni by the Qwen team, which pioneered unified multimodal understanding.

License

Apache 2.0 • No data collection • Privacy-first