SmolVLM2-256M-Video-Instruct
| Property | Value |
|---|---|
| Developer | HuggingFace |
| License | Apache 2.0 |
| Model Size | 256M parameters |
| GPU Memory | 1.38GB |
| Benchmark Score (Video-MME) | 33.7 |
What is SmolVLM2-256M-Video-Instruct?
SmolVLM2-256M-Video-Instruct is a lightweight multimodal AI model designed for efficient video and image analysis. Built on the Idefics3 architecture, it specializes in processing videos, images, and text inputs to generate meaningful text outputs. Despite its compact size of only 256M parameters, it delivers impressive performance across various multimedia understanding tasks while requiring minimal computational resources.
Implementation Details
The model is implemented using the Transformers library and requires specific dependencies including num2words, flash-attn, and the latest transformers package. It utilizes bfloat16 precision and Flash Attention 2 for optimal performance. The model processes input through a specialized processor that can handle interleaved media and text content.
- Trained on 3.3M samples across various modalities (34.4% image, 33% video, 20.2% text, 12.3% multi-image)
- Supports multiple input types including videos, single images, and multi-image scenarios
- Implements efficient attention mechanisms for faster processing
Core Capabilities
- Video content analysis and description
- Visual question answering
- Image comparison and similarity analysis
- Text transcription from visual content
- Multi-modal understanding and reasoning
Frequently Asked Questions
Q: What makes this model unique?
The model's standout feature is its efficiency-to-performance ratio, requiring only 1.38GB of GPU RAM while maintaining solid performance on video understanding tasks. This makes it ideal for resource-constrained environments and specific domain applications.
Q: What are the recommended use cases?
The model excels in video content analysis, image understanding, and multimodal reasoning tasks. However, it's not suitable for critical decision-making processes or generating images/videos. Ideal applications include content description, visual QA, and multimedia analysis tasks where resource efficiency is crucial.
