Qwen2.5-VL-3B-Instruct-AWQ
| Property | Value |
|---|---|
| Model Size | 3 Billion Parameters |
| Model Type | Vision-Language Model |
| Quantization | AWQ |
| Model URL | Hugging Face - Qwen/Qwen2.5-VL-3B-Instruct-AWQ |
What is Qwen2.5-VL-3B-Instruct-AWQ?
Qwen2.5-VL-3B-Instruct-AWQ is a compressed version of the Qwen2.5-VL vision-language model, utilizing AWQ quantization to maintain high performance while reducing computational requirements. This model represents a significant advancement in multimodal AI, capable of understanding and processing both images and text with remarkable efficiency.
Implementation Details
The model features a streamlined vision encoder with optimized ViT architecture, incorporating SwiGLU and RMSNorm alignments. It supports dynamic resolution and frame rate training for enhanced video understanding, with innovative mRoPE implementations for temporal dimension processing.
- Supports context lengths up to 32,768 tokens
- Implements window attention for improved training and inference speeds
- Features flexible image resolution handling with customizable pixel ranges
- Utilizes advanced quantization techniques for efficient deployment
Core Capabilities
- Advanced visual recognition of objects, texts, charts, and layouts
- Agent-like capabilities for computer and phone use scenarios
- Long video understanding with event capture functionality
- Precise visual localization with bounding box and point generation
- Structured output generation for financial and commercial documents
Frequently Asked Questions
Q: What makes this model unique?
The model combines efficient quantization with comprehensive vision-language capabilities, offering a balance between performance and resource usage. Its ability to handle multiple visual formats and generate structured outputs makes it particularly versatile.
Q: What are the recommended use cases?
The model excels in document analysis, visual content understanding, video processing, and agent-based tasks. It's particularly suitable for applications requiring efficient processing of mixed media content while maintaining high accuracy.
