Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit
| Property | Value |
|---|---|
| Parameter Count | 32 Billion |
| Model Type | Vision-Language Model |
| Architecture | Transformer-based with ViT and SwiGLU |
| Paper | arXiv:2502.13923 |
What is Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit?
This is a 4-bit quantized version of the Qwen2.5-VL-32B model, optimized for efficient deployment while maintaining high performance. It's a multimodal model capable of understanding images, videos, and text, featuring enhanced mathematical reasoning and problem-solving capabilities through reinforcement learning.
Implementation Details
The model implements a streamlined vision encoder with window attention in ViT, optimized with SwiGLU and RMSNorm. It supports dynamic resolution and frame rate training for video understanding, with mRoPE temporal alignment for precise moment identification.
- Supports context length up to 32,768 tokens
- Implements YaRN for enhanced model length extrapolation
- Features dynamic FPS sampling for video comprehension
- Optimized for 4-bit quantization using unsloth's techniques
Core Capabilities
- Advanced visual recognition of objects, texts, charts, and layouts
- Visual agent functionality for computer and phone use simulation
- Long video understanding (over 1 hour) with event capturing
- Structured output generation for financial and commercial applications
- Precise object localization with bounding box and point generation
Frequently Asked Questions
Q: What makes this model unique?
The model combines advanced visual-language capabilities with 4-bit quantization, making it both powerful and efficient. It excels in mathematical reasoning, video understanding, and structured output generation while maintaining a smaller memory footprint.
Q: What are the recommended use cases?
The model is ideal for applications requiring complex visual analysis, document processing, video understanding, and mathematical problem-solving. It's particularly suitable for deployment in resource-constrained environments due to its 4-bit quantization.
