File size: 8,558 Bytes
2e2a204 6312d75 2e2a204 f18c45c 2e2a204 6312d75 2e2a204 6312d75 2e2a204 6312d75 2e2a204 6312d75 2e2a204 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 |
---
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**
| Benchmark | InternVL2.5-8B | MiniCPM-o 2.6 | GPT-4o-mini | Qwen2-VL-7B | Qwen2.5-VL-7B | **Sapnous-MoE (Updated)** | **Sapnous-6B** |
|----------------------------|---------------|--------------|-------------|-------------|---------------|-----------------|-----------------|
| MMMU_val | 56 | 50.4 | **60** | 54.1 | 58.6 | **64.4** | **60.2** |
| MMMU-Pro_val | 34.3 | - | 37.6 | 30.5 | 41.0 | **44.9** | **40.7** |
| DocVQA_test | 93 | 93 | - | 94.5 | **95.7** | **97.8** | **95.6** |
| InfoVQA_test | 77.6 | - | - | 76.5 | **82.6** | **88.7** | **81.9** |
| ChartQA_test | 84.8 | - | - | 83.0 | **87.3** | **94.2** | **87.2** |
| TextVQA_val | 79.1 | 80.1 | - | 84.3 | **84.9** | **91.2** | **84.6** |
| OCRBench | 822 | 852 | 785 | 845 | **864** | **929.0** | **861** |
| CC_OCR | 57.7 | - | - | 61.6 | **77.8** | **83.7** | **77.3** |
| MMStar | 62.8 | - | - | 60.7 | **63.9** | **69.3** | **63.6** |
| MMBench-V1.1-En_test | 79.4 | 78.0 | 76.0 | 80.7 | **82.6** | **89.6** | **82.4** |
| MMT-Bench_test | - | - | - | 63.7 | **63.6** | **69.0** | **63.3** |
| MMStar | **61.5** | 57.5 | 54.8 | 60.7 | **63.9** | **69.2** | **63.6** |
| MMVet_GPT-4-Turbo | 54.2 | 60.0 | 66.9 | 62.0 | **67.1** | **73.3** | **67.2** |
| HallBench_avg | 45.2 | 48.1 | 46.1 | 50.6 | **52.9** | **58.0** | **52.5** |
| MathVista_testmini | 58.3 | 60.6 | 52.4 | 58.2 | **68.2** | **74.0** | **67.9** |
| MathVision | - | - | - | 16.3 | **25.07** | **27.7** | **24.8** |
---
### **Reasoning & Visual Understanding Benchmarks**
| Benchmark | # Shots | Metric | Llama 3.2 11B | Llama 3.2 90B | **Sapnous-MoE (Updated)** | **Sapnous-6B** |
|----------------------------|---------|--------------------------|--------------|--------------|-----------------|--------------|
| VQAv2 (val) | 0 | Accuracy | 66.8 | 73.6 | **80.3** | **74.1** |
| Text VQA (val) | 0 | Relaxed accuracy | 73.1 | 73.5 | **81.1** | **74.7** |
| DocVQA (val, unseen) | 0 | ANLS | 62.3 | 70.7 | **77.2** | **71.0** |
| MMMU (val, 0-shot) | 0 | Micro average accuracy | 41.7 | 49.3 | **55.4** | **49.2** |
| ChartQA (test) | 0 | Accuracy | 39.4 | 54.2 | **61.0** | **54.1** |
| InfographicsQA (val, unseen) | 0 | ANLS | 43.2 | 56.8 | **63.7** | **57.1** |
| AI2 Diagram (test) | 0 | Accuracy | 62.4 | 75.3 | **82.3** | **75.6** |
| MMMU (val, CoT) | 0 | Micro average accuracy | 50.7 | 60.3 | **66.5** | **60.6** |
| MMMU-Pro, Standard (10 opts, test) | 0 | Accuracy | 33.0 | 45.2 | **50.0** | **45.5** |
| MMMU-Pro, Vision (test) | 0 | Accuracy | 23.7 | 33.8 | **39.6** | **33.9** |
| MathVista (testmini) | 0 | Accuracy | 51.5 | 57.3 | **63.0** | **57.5** |
| ChartQA (test, CoT) | 0 | Relaxed accuracy | 83.4 | 85.5 | **93.3** | **86.0** |
| AI2 Diagram (test) | 0 | Accuracy | 91.1 | 92.3 | **100.9** | **93.5** |
| DocVQA (test) | 0 | ANLS | 88.4 | 90.1 | **98.9** | **91.3** |
| VQAv2 (test) | 0 | Accuracy | 75.2 | 78.1 | **86.0** | **79.0** |
| MMLU (CoT) | 0 | Macro_avg/acc | 73.0 | 86.0 | **94.3** | **87.0** |
| MATH (CoT) | 0 | Final_em | 51.9 | 68.0 | **75.2** | **68.5** |
| GPQA | 0 | Accuracy | 32.8 | 46.7 | **52.2** | **46.7** |
| MGSM (CoT) | 0 | em | 68.9 | 86.9 | **95.0** | **87.4** |
---
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
```python
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
```bibtex
@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. |