Spec Vision V1

🚀 Spec-Vision-V1

Spec-Vision-V1 is a lightweight, state-of-the-art open multimodal model. It focuses on high-quality, reasoning-dense data in both text and vision, enabling deep integration of visual and textual data.

🚀 Quick Start

Spec-Vision-V1 is a lightweight, state-of-the-art open multimodal model built on diverse datasets. It supports a 128K context length and has undergone a rigorous enhancement process. To get started, you need to install the required dependencies and then you can load the model for inference.

✨ Features

• 🖼️ Multimodal Processing: Seamlessly combines image and text inputs.
• ⚡ Transformer-Based Architecture: High efficiency in vision-language understanding.
• 📝 Optimized for VQA & Captioning: Excels in answering visual questions and generating descriptions.
• 📥 Pre-trained Model: Available for inference and fine-tuning.

📦 Installation

To use Spec-Vision-V1, install the required dependencies:

pip install transformers torch torchvision pillow

💻 Usage Examples

Basic Usage

from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image
import torch

# Load the model and processor
model_name = "Spec-Vision-V1"
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)

# Load an example image
image = Image.open("example.jpg")

# Input text prompt
text = "Describe the image in detail."

# Process inputs
inputs = processor(images=image, text=text, return_tensors="pt")

# Generate output
with torch.no_grad():
    outputs = model(**inputs)

# Print the generated text
print(outputs)

📚 Documentation

Model Specifications

Applications

Benchmark Results

BLINK Benchmark

A benchmark with 14 visual tasks that humans can solve very quickly but are still hard for current multimodal LLMs.

Video-MME Benchmark

A benchmark that comprehensively assesses the capabilities of multimodal LLMs in processing video data, covering a wide range of visual domains, temporal durations, and data modalities.

Model Training Details

📄 License

Spec-Vision-V1 is released under the MIT license.

📖 Citation

If you use Spec-Vision-V1 in your research or application, please cite:

@article{SpecVision2025,
  title={Spec-Vision-V1: A Vision-Language Transformer Model},
  author={SVECTOR},
  year={2025},
  journal={SVECTOR Research}
}

📬 Contact

For support or inquiries, reach out to SVECTOR:

• 🌐 Website: svector.co.in
• 📧 Email: Research@svector.co.in
• ✨ GitHub: SVECTOR GitHub