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README.md

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 NVILA-HD-Video is a Multi-modal Large Language Model with 8B parameters that understands and answers questions about videos with up to 4K resolution and 1K frames.
 
-Specifically, NVILA-HD-Video uses [AutoGaze](nvidia/AutoGaze) to reduce redundant patches in a video before running the ViT or LLM. Empirically, AutoGaze can reduce #tokens in in a video by up to 100x, reducing the latency of ViT/LLM by up to 19x/10x. This enables NVILA-HD-Video to efficiently scale to 4K-resolution, 1K-frame videos and achieve improved performance on benchmarks such as VideoMME and state-of-the-art performance on HLVid, a high-resolution long-form video benchmark proposed in this work as well.
+Specifically, NVILA-HD-Video uses [AutoGaze](https://huggingface.co/nvidia/AutoGaze) to reduce redundant patches in a video before running the ViT or LLM. Empirically, AutoGaze can reduce #tokens in in a video by up to 100x, reducing the latency of ViT/LLM by up to 19x/10x. This enables NVILA-HD-Video to efficiently scale to 4K-resolution, 1K-frame videos and achieve improved performance on benchmarks such as VideoMME and state-of-the-art performance on HLVid, a high-resolution long-form video benchmark proposed in this work as well.
 
 This model is for research and development only.