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FastVLM-0.5B

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shirakiin commited on Dec 3, 2025

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@@ -18,7 +18,7 @@ This model card provides *FastVLM-0.5B converted for LiteRT* that are ready for
 FastVLM was introduced in [FastVLM: Efficient Vision Encoding for Vision Language Models](https://www.arxiv.org/abs/2412.13303). *(CVPR 2025)*, this model demonstrates improvement in time-to-first-token (TTFT) with performance and is suitable for edge device deployment.
-The model is also converted for Qualcomm NPUs, see more details in this [blogpost](https://developers.googleblog.com/unlocking-peak-performance-on-qualcomm-npu-with-litert/).
 *Disclaimer*: This model converted for LiteRT is licensed under the [Apple Machine Learning Research Model License Agreement](https://huggingface.co/apple/deeplabv3-mobilevit-small/blob/main/LICENSE). The model is converted and quantized from PyTorch model weight into the LiteRT/Tensorflow-Lite format (no retraining or further customization).

 FastVLM was introduced in [FastVLM: Efficient Vision Encoding for Vision Language Models](https://www.arxiv.org/abs/2412.13303). *(CVPR 2025)*, this model demonstrates improvement in time-to-first-token (TTFT) with performance and is suitable for edge device deployment.
+The model is supported on CPU, GPU and Qualcomm NPUs. For Qualcomm integration, see more details in this [blogpost](https://developers.googleblog.com/unlocking-peak-performance-on-qualcomm-npu-with-litert/).
 *Disclaimer*: This model converted for LiteRT is licensed under the [Apple Machine Learning Research Model License Agreement](https://huggingface.co/apple/deeplabv3-mobilevit-small/blob/main/LICENSE). The model is converted and quantized from PyTorch model weight into the LiteRT/Tensorflow-Lite format (no retraining or further customization).