ToriiGate-v0.4-7B
| Property | Value |
|---|---|
| Model Size | 7 Billion Parameters |
| Base Architecture | Qwen2-VL |
| Model Type | Vision-Language Model (VLM) |
| Author | Minthy |
| Model URL | https://huggingface.co/Minthy/ToriiGate-v0.4-7B |
What is ToriiGate-v0.4-7B?
ToriiGate-v0.4-7B is a specialized vision-language model designed for captioning anime pictures, digital artworks, and various images. Built upon Qwen2-VL and fine-tuned with over 900,000 artwork-caption pairs, it represents a significant advancement in understanding complex scenes, cultural concepts, and character interactions in artistic content.
Implementation Details
The model implements multiple captioning modes including structured output (JSON/Markdown), pre-defined caption variants, long/short descriptions, and bounding box detection. It features flexible grounding capabilities through booru tags, character lists, and trait descriptions to enhance accuracy.
- Built on Qwen2-VL architecture with 7B parameters
- Trained on 900k+ anime/artwork samples
- Supports multiple output formats and grounding methods
- Available in various quantization options (8bpw, 6bpw, 4bpw)
Core Capabilities
- Advanced anime and digital art understanding
- Accurate character name recognition and usage
- Structured output generation with character-specific details
- Scene composition and atmosphere description
- Support for multiple captioning styles and formats
- Caption review and correction functionality
Frequently Asked Questions
Q: What makes this model unique?
ToriiGate-v0.4-7B is currently the only opensource small-sized VLM that effectively handles multiple character names and provides structured outputs specifically designed for anime and artwork content. Its flexible grounding system and multiple captioning modes make it versatile for various use cases.
Q: What are the recommended use cases?
The model is ideal for automated artwork captioning, dataset creation for AI training, detailed scene analysis of anime/digital art, and generating structured descriptions for character-centric images. It's particularly useful when accurate character recognition and detailed scene description are required.
