Instructions to use c299m/tomato-grasping-gr00t with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use c299m/tomato-grasping-gr00t with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import GR00T_N1_5 model = GR00T_N1_5.from_pretrained("c299m/tomato-grasping-gr00t", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from c299m/tomato-grasping-gr00t: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/c299m/tomato-grasping-gr00t/resolve/main/training_args.bin
- Command line
-
hf download hf://c299m/tomato-grasping-gr00t/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/c299m/tomato-grasping-gr00t/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- c1c4ccf2247fc1d6d879331b56f3b9b6c349a3467b9e2b47edef25e2779c684f
- Size of remote file:
- 5.37 kB
- SHA256:
- 11f8a1804fd8f9f8ed143d4997a5a662cbad194fb39e60f6df0bb3ce87229a46
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