Instructions to use MVRL/prithvi-100M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MVRL/prithvi-100M with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MVRL/prithvi-100M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Push model using huggingface_hub.
Browse files- README.md +9 -0
- config.json +15 -0
- model.safetensors +3 -0
README.md
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---
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tags:
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- pytorch_model_hub_mixin
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- model_hub_mixin
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---
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This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
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- Library: [More Information Needed]
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- Docs: [More Information Needed]
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config.json
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{
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"decoder_depth": 8,
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"decoder_embed_dim": 512,
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"decoder_num_heads": 16,
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"depth": 12,
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"embed_dim": 768,
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"img_size": 224,
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"in_chans": 6,
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"mlp_ratio": 4.0,
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"norm_pix_loss": false,
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"num_frames": 3,
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"num_heads": 12,
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"patch_size": 16,
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"tubelet_size": 1
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:7f21a0d1fba0fde4565627d148cafb4d6ee1e50296823db61322382405baa234
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size 453598032
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