Model Summary

Spec-Vision-V1 is a lightweight, state-of-the-art open multimodal model built on datasets that include synthetic data and filtered publicly available sources, with a focus on high-quality, reasoning-dense data in both text and vision. The model belongs to the SpecVision family and supports a 128K context length (in tokens). It has undergone a rigorous enhancement process, incorporating supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures.

🚀 Model Overview

Spec-Vision-V1 is built for deep integration of visual and textual data, enabling it to understand and process images in combination with natural language. The model has been trained on a diverse dataset containing images with associated captions, descriptions, and contextual information.

✨ Key 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

📥 Load the Model

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)

📊 Model Specifications

Attribute	Description
Model Name	Spec-Vision-V1
Architecture	Transformer-based Vision-Language Model
Pretrained	✅ Yes
Dataset	Trained on diverse image-text pairs
Framework	PyTorch & Hugging Face Transformers

🎯 Applications

Task	Description
🖼️ Image Captioning	Generates detailed descriptions for input images.
🧐 Visual Question Answering	Answers questions about images.
🔎 Image-Text Matching	Determines the relevance of an image to a given text.
🌍 Scene Understanding	Extracts insights from complex visual data.

BLINK Benchmark

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

Benchmark	Spec-Vision-V1	LlaVA-Interleave-Qwen-7B	InternVL-2-4B	InternVL-2-8B	Gemini-1.5-Flash	GPT-4o-mini	Claude-3.5-Sonnet	Gemini-1.5-Pro	GPT-4o
Art Style	87.2	62.4	55.6	52.1	64.1	70.1	59.8	70.9	73.3
Counting	54.2	56.7	54.2	66.7	51.7	55.0	59.2	65.0	65.0
Forensic Detection	92.4	31.1	40.9	34.1	54.5	38.6	67.4	60.6	75.8
Functional Correspondence	29.2	34.6	24.6	24.6	33.1	26.9	33.8	31.5	43.8
IQ Test	25.3	26.7	26.0	30.7	25.3	29.3	26.0	34.0	19.3
Jigsaw	68.0	86.0	55.3	52.7	71.3	72.7	57.3	68.0	67.3
Multi-View Reasoning	54.1	44.4	48.9	42.9	48.9	48.1	55.6	49.6	46.6
Object Localization	49.2	54.9	53.3	54.1	44.3	57.4	62.3	65.6	68.0
Relative Depth	69.4	77.4	63.7	67.7	57.3	58.1	71.8	76.6	71.0
Relative Reflectance	37.3	34.3	32.8	38.8	32.8	27.6	36.6	38.8	40.3
Semantic Correspondence	36.7	31.7	31.7	22.3	32.4	31.7	45.3	48.9	54.0
Spatial Relation	65.7	75.5	78.3	78.3	55.9	81.1	60.1	79.0	84.6
Visual Correspondence	53.5	40.7	34.9	33.1	29.7	52.9	72.1	81.4	86.0
Visual Similarity	83.0	91.9	48.1	45.2	47.4	77.8	84.4	81.5	88.1
Overall	57.0	53.1	45.9	45.4	45.8	51.9	56.5	61.0	63.2

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.

Benchmark	Spec-Vision-V1	LlaVA-Interleave-Qwen-7B	InternVL-2-4B	InternVL-2-8B	Gemini-1.5-Flash	GPT-4o-mini	Claude-3.5-Sonnet	Gemini-1.5-Pro	GPT-4o
Short (<2min)	60.8	62.3	60.7	61.7	72.2	70.1	66.3	73.3	77.7
Medium (4-15min)	47.7	47.1	46.4	49.6	62.7	59.6	54.7	61.2	68.0
Long (30-60min)	43.8	41.2	42.6	46.6	52.1	53.9	46.6	53.2	59.6
Overall	50.8	50.2	49.9	52.6	62.3	61.2	55.9	62.6	68.4

🏗️ Model Training Details

Parameter	Value
Batch Size	16
Optimizer	AdamW
Learning Rate	5e-5
Training Steps	100k
Loss Function	CrossEntropyLoss
Framework	PyTorch & Transformers

📜 License

Spec-Vision-V1 is released under the MIT.

📖 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: [email protected]
✨ GitHub: SVECTOR GitHub

SVECTOR-CORPORATION
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Spec-Vision-V1