zen-family/tools/conversion/gguf-conversion/output/zen-omni-captioner/zen-omni-captioner_MODEL_CARD.md

zen-omni-captioner

Model Details

• Model Type: vision-language
• Version: 1.5.0
• Author: Hanzo AI
• License: Apache-2.0
• Base Model: Qwen3-VL

Architecture

• Context Length: 16,384 tokens
• Vocabulary Size: 50,000
• Hidden Size: 1536
• Number of Layers: 24
• Number of Heads: 16

Capabilities

• image-captioning
• visual-description
• scene-understanding

Special Tokens

{
  "<s>": "bos_token",
  "</s>": "eos_token",
  "<pad>": "pad_token",
  "<|image|>": "image_token",
  "<|caption|>": "caption_start",
  "<|/caption|>": "caption_end",
  "<|description|>": "description_start",
  "<|/description|>": "description_end"
}

Training

• Training Data: Hanzo vision-language dataset
• Has Thinking: No

Usage

This model has been converted to GGUF format for use with llama.cpp.

Example Usage

# Run with llama.cpp
./llama-cli -m zen-omni-captioner-Q5_K_M.gguf \
    --ctx-size 16384 \
    --temp 0.7 \
    --top-p 0.9 \
    --prompt "Your prompt here"

Quantization Variants

Multiple quantization levels are available:

• Q4_K_M: Balanced quality and size (~4 bits per weight)
• Q5_K_M: Higher quality (~5 bits per weight)
• Q6_K: Even higher quality (~6 bits per weight)
• Q8_0: Near-lossless quality (~8 bits per weight)
• FP16: Full precision (16 bits per weight)

Choose based on your hardware capabilities and quality requirements.