SmolVLM-256M-Instruct
| Property | Value |
|---|---|
| Model Size | 256M parameters |
| License | Apache 2.0 |
| Type | Multi-modal (image+text) |
| Architecture | Based on Idefics3 |
| GPU Requirements | <1GB VRAM |
What is SmolVLM-256M-Instruct?
SmolVLM-256M-Instruct is a groundbreaking compact multimodal model that represents the smallest of its kind in the world. Developed by Hugging Face, it's designed to process both images and text while maintaining impressive performance despite its small size. The model can handle tasks like image description, visual question answering, and text transcription with remarkable efficiency.
Implementation Details
The model implements several innovative technical features to achieve its compact size while maintaining functionality. It uses a radical image compression technique and employs 64 visual tokens to encode 512×512 image patches. The architecture includes a streamlined 93M parameter vision encoder, significantly smaller than its larger counterparts.
- Efficient image compression system for reduced memory usage
- 64 visual tokens for encoding 512×512 image patches
- 93M parameter vision encoder (reduced from 400M)
- Special tokens for efficient subimage division
- Supports bfloat16 and 4/8-bit quantization
Core Capabilities
- Image captioning and description
- Visual question answering
- Text transcription from images
- Multi-image reasoning
- Document understanding (25% training focus)
- Chart comprehension
Frequently Asked Questions
Q: What makes this model unique?
SmolVLM-256M-Instruct stands out for being the smallest multimodal model that can handle both image and text processing while requiring less than 1GB of GPU RAM. Its efficiency-focused design makes it ideal for on-device applications where resources are limited.
Q: What are the recommended use cases?
The model is best suited for applications requiring image understanding and text generation in resource-constrained environments. It excels in image captioning, visual QA, and document understanding tasks, but should not be used for critical decision-making or high-stakes scenarios.
