SmolVLM: A Small Yet Mighty Vision Language Model

Introduction

SmolVLM is a compact yet powerful Vision-Language Model (VLM) designed to achieve high performance while maintaining a lightweight architecture. It is particularly optimized for efficiency, making it suitable for deployment in resource-constrained environments. In this article, we will explore SmolVLM’s architecture, training methodology, technical benchmarks, and its applications in real-world scenarios.

Architecture

The architecture of SmolVLM consists of several key components, as illustrated in the provided image:

• Vision Encoder: Uses a lightweight convolutional or transformer-based backbone (e.g., Swin Transformer) to extract feature representations from images. It outputs hidden states that contain the image’s encoded information.
• Modality Projection + Pooling: Projects image embeddings into a lower-dimensional space and applies adaptive pooling to ensure optimal alignment with textual representations.
• LLM (Large Language Model): Utilizes a transformer-based decoder (e.g., LLaMA, GPT-style) to process both image-derived and text-derived tokens, allowing for multimodal reasoning and response generation.
• Token Processing: The architecture integrates visual features and…