X-Ray_Alpha
| Property | Value |
|---|---|
| Author | SicariusSicariiStuff |
| VRAM Required | 15.9GB (FP16) |
| Model URL | https://huggingface.co/SicariusSicariiStuff/X-Ray_Alpha |
| Status | Pre-alpha POC |
What is X-Ray_Alpha?
X-Ray_Alpha is a groundbreaking pre-alpha proof-of-concept for an uncensored vision model, positioning itself as only the second such model after ToriiGate. Unlike other vision models that rely on stock vision components with fine-tuned LLMs, X-Ray_Alpha represents a fully trained, uncensored vision model designed for comprehensive image analysis and tagging.
Implementation Details
The model is implemented with careful consideration for both vision and text capabilities, utilizing a massive corpus of high-quality human (~60%) and synthetic data. It requires 15.9GB VRAM for FP16 inference and is designed to run through a simple Python interface.
- Fully uncensored vision processing capabilities
- Detailed and in-depth image descriptions
- Partially uncensored text generation while maintaining intelligence
- Implementation via Python with transformers library v4.49.0-Gemma-3
Core Capabilities
- Comprehensive image tagging for various applications
- Detailed content description generation
- Support for LORA creation and diffusion model pretraining
- Nuanced content moderation without corporate restrictions
- Art and historical artifact analysis including sensitive content
Frequently Asked Questions
Q: What makes this model unique?
X-Ray_Alpha stands out as one of the only truly trained uncensored vision models, offering genuine vision processing capabilities without corporate content restrictions. It enables users to make their own content moderation decisions and supports various use cases including art analysis and dataset creation.
Q: What are the recommended use cases?
The model is particularly suited for image tagging, LORA creation, diffusion model pretraining, and content analysis where nuanced understanding is required. It's especially valuable for researchers and developers working on dataset creation and content classification tasks.
