license_name: apache-2.0 language:

• en pipeline_tag: image-text-to-text tags:
• multimodal library_name: transformers base_model:
• Sapnous/Sapnous-6B license: apache-2.0

Sapnous-6B: A Vision-Language Model for Enhanced World Perception

Sapnous-6B is a state-of-the-art vision-language model designed to enhance perception and understanding of the world through advanced multimodal capabilities. This model builds upon the success of previous vision-language architectures while introducing novel improvements in performance and efficiency.

Model Architecture

• Base Architecture: 6B parameters
• Hidden Size: 4096
• Attention Heads: 32
• Key/Value Heads: 8
• Hidden Layers: 28
• Window Size: 32768
• Vision Encoder:
  • Depth: 32 layers
  • Hidden Size: 1280
  • Attention Heads: 16
  • Patch Size: 14x14
  • Window Size: 112

Scores

📊 Benchmark Results

Multimodal Benchmarks

Reasoning & Visual Understanding Benchmarks

The model is distributed across 5 safetensors files for efficient loading and memory management. Each file contains specific layers and weights as documented in the model.safetensors.index.json.

Usage

from transformers import pipeline
import requests
from PIL import Image
from io import BytesIO

def process_image_from_url(image_url, text_prompt):
    """Processes an image from a URL using a Transformers pipeline."""
    try:
        # Fetch the image from the URL
        response = requests.get(image_url, stream=True)
        response.raise_for_status()  # Raise an exception for bad status codes (4xx or 5xx)

        # Open the image using PIL
        image = Image.open(BytesIO(response.content))

        # Create the input for the pipeline
        inputs = {"image": image, "text": text_prompt}

        # Initialize the pipeline
        pipe = pipeline("image-text-to-text", model="Sapnous-AI/Sapnous-VR-6B", trust_remote_code=True)

        # Process the image and text
        result = pipe(inputs)
        return result

    except requests.exceptions.RequestException as e:
        print(f"Error fetching image: {e}")
        return None
    except Exception as e:
        print(f"An error occurred: {e}")
        return None

# Example usage
image_url = "example.com" #replace with your image url.
text_prompt = "What is in this image?"

result = process_image_from_url(image_url, text_prompt)

if result:
    print(result)

Model Capabilities

• Multi-modal understanding and generation
• Enhanced visual perception with advanced vision encoder
• Efficient processing of long sequences
• Robust performance across various vision-language tasks

Citations

@misc{sapnous-6b,
    title = {Sapnous-6B},
    author = {Sapnous AI Team},
    year = {2025}
}

@article{Sapnous6B,
    title={Sapnous-6B: Enhancing Vision-Language Model's Perception of the World at Any Resolution},
    author={Sapnous AI Team},
    year={2025}
}

@article{Sapnous-VR,
    title={Sapnous-VR: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond},
    author={Sapnous AI Team},
    year={2025}
}

License

Please refer to the LICENSE file for terms of use and distribution.