SmolVLM2-2.2B-Instruct
| Property | Value |
|---|---|
| Developer | HuggingFaceTB |
| Model Size | 2.2B parameters |
| License | Apache 2.0 |
| GPU Requirements | 5.2GB VRAM |
| Architecture | Based on Idefics3 |
What is SmolVLM2-2.2B-Instruct?
SmolVLM2-2.2B-Instruct is a revolutionary lightweight multimodal model designed for comprehensive video and image analysis. Despite its compact size, it demonstrates impressive capabilities across various tasks, including video understanding, visual question answering, and image-text processing. The model was trained on a diverse dataset of 3.3M samples, combining video, image, and text data from multiple high-quality sources.
Implementation Details
The model leverages a shape-optimized SigLIP as its image encoder and incorporates SmolLM2 for text decoding. It demonstrates strong performance across multiple benchmarks, achieving 51.5% on Mathvista, 72.9% on OCRBench, and 52.1% on Video-MME evaluations.
- Efficient architecture requiring only 5.2GB of GPU RAM for video inference
- Support for multiple input modalities including videos, images, and text
- Built-in flash attention 2 implementation for optimal performance
- Comprehensive training on diverse datasets including LlaVa Onevision, M4-Instruct, and VideoStar
Core Capabilities
- Video content analysis and description
- Visual question answering across multiple images
- Text transcription and OCR capabilities
- Interleaved processing of multiple media types
- Real-time video understanding and analysis
Frequently Asked Questions
Q: What makes this model unique?
The model's primary strength lies in its ability to deliver high-performance multimodal processing while maintaining a remarkably small footprint, making it ideal for resource-constrained environments and real-world applications.
Q: What are the recommended use cases?
The model excels in video content analysis, image question answering, multi-image comparison, and text transcription tasks. However, it should not be used for critical decision-making processes or generating unreliable factual content.
