Instructions to use anhdao69/SimpleMemVLN-R2R-RxR15deg-FullContext-CandidateLogits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anhdao69/SimpleMemVLN-R2R-RxR15deg-FullContext-CandidateLogits with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("anhdao69/SimpleMemVLN-R2R-RxR15deg-FullContext-CandidateLogits", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download epoch-1/pytorch_model.bin from anhdao69/SimpleMemVLN-R2R-RxR15deg-FullContext-CandidateLogits: direct link, hf CLI and curl.
- Browser
- Download file 10.4 GB
-
https://huggingface.co/anhdao69/SimpleMemVLN-R2R-RxR15deg-FullContext-CandidateLogits/resolve/main/epoch-1/pytorch_model.bin
- Command line
-
hf download hf://anhdao69/SimpleMemVLN-R2R-RxR15deg-FullContext-CandidateLogits/epoch-1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/anhdao69/SimpleMemVLN-R2R-RxR15deg-FullContext-CandidateLogits/resolve/main/epoch-1/pytorch_model.bin
10.4 GB
- Xet hash:
- a5dab93b204b557c74f480c83cc46b4155ea93be2da8a9ed54312682f532afd0
- Size of remote file:
- 10.4 GB
- SHA256:
- 9c00d98bbb964d213005725d57b0e099413ba3a91e7da8a00cfaa6c89f8a22bf
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