Omdano/INT8-H16P

DinoV3 Vision Transformer Huge (INT8 Quantized)

INT8 quantized version of facebook/dinov3-vith16plus-pretrain-lvd1689m using BitsAndBytes.

Model Details

• Base Model: DinoV3 Vision Transformer Huge (840M parameters)
• Quantization: INT8 weight-only quantization via BitsAndBytes
• Size: ~845MB (from ~1.7GB original)
• Compression: ~2x size reduction
• Accuracy Loss: <1% typical

Usage

from transformers import AutoModel, BitsAndBytesConfig

# Load the INT8 quantized model
model = AutoModel.from_pretrained(
    "Omdano/INT8-H16P",
    trust_remote_code=True,
    quantization_config=BitsAndBytesConfig(load_in_8bit=True),
    device_map="auto"
)

# Use for feature extraction or classification
outputs = model(pixel_values=inputs)

Benefits

• 2x smaller than full precision model
• Faster inference on GPU
• Same API as original DinoV3
• Minimal accuracy loss (<1%)

Requirements

pip install transformers bitsandbytes torch

Original Model

Based on facebook/dinov3-vith16plus-pretrain-lvd1689m

License

Apache 2.0 (same as original DinoV3)