SmolVLM2-500M-Video-Instruct
| Property | Value |
|---|---|
| Developer | HuggingFace |
| Model Type | Multi-modal (video/image/text) |
| License | Apache 2.0 |
| Memory Requirement | 1.8GB GPU RAM |
| Benchmark Score (Video-MME) | 42.2 |
What is SmolVLM2-500M-Video-Instruct?
SmolVLM2-500M-Video-Instruct is a compact yet powerful multimodal model designed for analyzing video and image content. Built on the Idefics3 architecture, it excels at processing videos, images, and text inputs to generate meaningful text outputs. Despite its modest size of 500M parameters, it delivers impressive performance on complex tasks while maintaining minimal computational requirements.
Implementation Details
The model is trained on a diverse dataset of 3.3M samples, including video (33%), image (34.4%), text (20.2%), and multi-image (12.3%) content. It leverages SigLIP as an image encoder and SmolLM2 for text decoding, enabling efficient processing of various media types.
- Supports both single and multi-image analysis
- Processes video content with minimal GPU memory (1.8GB)
- Implements flash attention 2 for optimal performance
- Uses bfloat16 precision for efficient computation
Core Capabilities
- Video and image content description
- Visual question answering
- Multi-image comparison and analysis
- Text transcription from visual content
- Interleaved media processing
Frequently Asked Questions
Q: What makes this model unique?
Its exceptional efficiency-to-performance ratio, requiring only 1.8GB of GPU RAM while maintaining competitive benchmark scores (42.2 on Video-MME, 47.3 on MLVU), makes it ideal for resource-constrained environments.
Q: What are the recommended use cases?
The model is well-suited for video content analysis, image captioning, visual QA, and multi-image comparison tasks. However, it should not be used for critical decision-making, surveillance, or generating factual content without verification.
