Hugging Face has introduced two new models in their SmolVLM family, which are smaller and more efficient versions of the original model. The 256M and 500M models have fewer parameters than their larger counterparts, making them suitable for devices with limited memory or power constraints. These models can still generate text with high quality and accuracy. According to Hugging Face, these models were trained on a large corpus of text data and fine-tuned on various tasks such as language translation and question-answering. --- Why it matters: These smaller models are important for researchers who need to deploy AI models in resource-constrained environments, such as edge devices or low-power IoT devices. They can also be used for research purposes where computational resources are limited. Source: https://huggingface.co/blog/smolervlm