Instructions to use xiaomoguhzz/VisionEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiaomoguhzz/VisionEncoder with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xiaomoguhzz/VisionEncoder", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add V9 good_manifest (decode-probed video list) for collaborator repro
Browse files
.gitattributes
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@@ -53,3 +53,4 @@ ms-swift-data/video_sft_full_v800k.json filter=lfs diff=lfs merge=lfs -text
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ms-swift-data/video_sft_full_v800k_sharegpt.json filter=lfs diff=lfs merge=lfs -text
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ms-swift-data/video_sft_small_10pct.json filter=lfs diff=lfs merge=lfs -text
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ms-swift-data/video_sft_small_10pct_sharegpt.json filter=lfs diff=lfs merge=lfs -text
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ms-swift-data/video_sft_full_v800k_sharegpt.json filter=lfs diff=lfs merge=lfs -text
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ms-swift-data/video_sft_small_10pct.json filter=lfs diff=lfs merge=lfs -text
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ms-swift-data/video_sft_small_10pct_sharegpt.json filter=lfs diff=lfs merge=lfs -text
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llava_video/llava_video_178k_v9_decord_good_manifest.json filter=lfs diff=lfs merge=lfs -text
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llava_video/llava_video_178k_v9_decord_good_manifest.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:89ffaedc1b93bf4c0a04a49482eea3d60067c08f58b3d6a19e0d5368c7dcf03b
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size 26312968
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