Instructions to use anhdao69/SimpleMemVLN-R2R-RxR15deg-Window8-B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anhdao69/SimpleMemVLN-R2R-RxR15deg-Window8-B with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("anhdao69/SimpleMemVLN-R2R-RxR15deg-Window8-B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download reports/train_results.json from anhdao69/SimpleMemVLN-R2R-RxR15deg-Window8-B: direct link, hf CLI and curl.
- Browser
- Download file 192 Bytes
-
https://huggingface.co/anhdao69/SimpleMemVLN-R2R-RxR15deg-Window8-B/resolve/main/reports/train_results.json
- Command line
-
hf download hf://anhdao69/SimpleMemVLN-R2R-RxR15deg-Window8-B/reports/train_results.json
-
curl -L -o train_results.json https://huggingface.co/anhdao69/SimpleMemVLN-R2R-RxR15deg-Window8-B/resolve/main/reports/train_results.json
192 Bytes
| { | |
| "epoch": 2.0, | |
| "total_flos": 0.0, | |
| "train_loss": 0.11737496930023178, | |
| "train_runtime": 227424.5161, | |
| "train_samples_per_second": 0.271, | |
| "train_steps_per_second": 0.034 | |
| } |