Instructions to use mamiksik/Testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mamiksik/Testing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mamiksik/Testing")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mamiksik/Testing") model = AutoModelForMaskedLM.from_pretrained("mamiksik/Testing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mamiksik/Testing: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/mamiksik/Testing/resolve/ec340b7540bd6a6e30d248b9ec8d98446140a7e0/training_args.bin
- Command line
-
hf download hf://mamiksik/Testing@ec340b7540bd6a6e30d248b9ec8d98446140a7e0/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mamiksik/Testing/resolve/ec340b7540bd6a6e30d248b9ec8d98446140a7e0/training_args.bin
3.52 kB
- Xet hash:
- 3459a1c01b823cc69f8cae0e914f0a8aa81befdc1ff39f0e6499fabb78ce3bda
- Size of remote file:
- 3.52 kB
- SHA256:
- eb33aa2ddbb80044926d2b94865cbc4d0a1af5708458f3ab0949aa9b9f1a4c74
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.