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-2/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-2/pytorch_model.bin
- Command line
-
hf download hf://anhdao69/SimpleMemVLN-R2R-RxR15deg-FullContext-CandidateLogits/epoch-2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/anhdao69/SimpleMemVLN-R2R-RxR15deg-FullContext-CandidateLogits/resolve/main/epoch-2/pytorch_model.bin
10.4 GB
- Xet hash:
- 98fc4a5bb5ba56670716ea603629f2c25b4251af4c7df7b9c2eca55fa1136c91
- Size of remote file:
- 10.4 GB
- SHA256:
- 6726f44245bfc1ceab739b72d86191003895450cbf17665980229eeb41d3b48a
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