SmolVLM 256M SmolVLM 256M is the smallest multimodal model in the world. It accepts arbitrary sequences of image and text inputs to produce text outputs. It's designed for efficiency. SmolVLM can answer questions about images, describe visual content, or transcribe text. Its lightweight architecture makes it suitable for on device applications while maintaining strong performance on multimodal tasks. It can run inference on one image with under 1GB of GPU RAM. Model Summary Developed by: Hugging Face 🤗 Model type: Multi modal model (image+text) Language(s) (NLP): English License: Apache 2.0 Architecture: Based on Idefics3 (see technical summary) Resources Demo: SmolVLM 256 Demo Blog: Blog post Uses SmolVLM can be used for inference on multimodal (image + text) tasks where the input comprises text queries along with one or more images. Text and images can be interleaved arbitrarily, enabling tasks like image captioning, visual question answering, and storytelling based on visual content. The model does not support image generation. To fine tune SmolVLM on a specific task, you can follow the fine tuning tutorial. Technical Summary SmolVLM leverages the lightweight SmolLM2 language m…
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