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 provenance/deepspeed.json from anhdao69/SimpleMemVLN-R2R-RxR15deg-Window8-B: direct link, hf CLI and curl.
- Browser
- Download file 433 Bytes
-
https://huggingface.co/anhdao69/SimpleMemVLN-R2R-RxR15deg-Window8-B/resolve/main/provenance/deepspeed.json
- Command line
-
hf download hf://anhdao69/SimpleMemVLN-R2R-RxR15deg-Window8-B/provenance/deepspeed.json
-
curl -L -o deepspeed.json https://huggingface.co/anhdao69/SimpleMemVLN-R2R-RxR15deg-Window8-B/resolve/main/provenance/deepspeed.json
433 Bytes
| { | |
| "bf16": {"enabled": "auto"}, | |
| "train_micro_batch_size_per_gpu": "auto", | |
| "train_batch_size": "auto", | |
| "gradient_accumulation_steps": "auto", | |
| "zero_force_ds_cpu_optimizer": false, | |
| "zero_optimization": { | |
| "stage": 2, | |
| "overlap_comm": true, | |
| "contiguous_gradients": true, | |
| "reduce_bucket_size": 50000000, | |
| "allgather_bucket_size": 50000000, | |
| "offload_optimizer": {"device": "cpu", "pin_memory": true} | |
| } | |
| } | |