Instructions to use underactuated/opt-350m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use underactuated/opt-350m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="underactuated/opt-350m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("underactuated/opt-350m") model = AutoModel.from_pretrained("underactuated/opt-350m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from underactuated/opt-350m: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/underactuated/opt-350m/resolve/main/training_args.bin
- Command line
-
hf download hf://underactuated/opt-350m/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/underactuated/opt-350m/resolve/main/training_args.bin
3.39 kB
- Xet hash:
- 0f1442094e4bab4da0f00c84df6f2de6ece318e3e6b48a8d98718c470677a0ac
- Size of remote file:
- 3.39 kB
- SHA256:
- 1548a0bcfcc6f57ac542712f63579c165b27ab7f78ee88be25139d23c5b60036
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