SmolVLM-Instruct
| Property | Value |
|---|---|
| Parameter Count | 2.25B |
| License | Apache 2.0 |
| Tensor Type | BF16 |
| Base Models | SmolLM2-1.7B-Instruct, SigLIP-so400m |
What is SmolVLM-Instruct?
SmolVLM-Instruct is a compact yet powerful multimodal model designed for efficient processing of image and text inputs. Developed by HuggingFace, it represents a significant advancement in lightweight multimodal AI, capable of handling tasks from image captioning to visual question answering while maintaining a relatively small footprint of 2.25B parameters.
Implementation Details
The model implements several innovative technical features, including radical image compression compared to its predecessor Idefics3. It utilizes 81 visual tokens to encode image patches of size 384×384, enabling efficient processing without compromising performance. The architecture combines the lightweight SmolLM2 language model with the shape-optimized SigLIP vision encoder.
- Efficient image compression system for reduced RAM usage
- Visual Token Encoding with 81 tokens per image patch
- Support for multiple input images with flexible interleaving of text
- Optimized for both CPU and GPU deployment
Core Capabilities
- Image captioning and description generation
- Visual question answering
- Document understanding (25% training focus)
- Chart comprehension and visual reasoning
- Multi-image storytelling
Frequently Asked Questions
Q: What makes this model unique?
SmolVLM-Instruct stands out for its efficient architecture that achieves impressive performance metrics while requiring minimal GPU RAM (5.02GB). It shows competitive results against larger models, scoring 38.8 on MMMU validation and 81.6 on DocVQA test.
Q: What are the recommended use cases?
The model excels in document understanding, image captioning, and visual question answering. It's particularly suitable for applications requiring efficient multimodal processing with limited computational resources. However, it should not be used for critical decision-making or high-stakes scenarios.
