Instructions to use soilSpoon/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use soilSpoon/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="soilSpoon/my_awesome_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("soilSpoon/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("soilSpoon/my_awesome_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from soilSpoon/my_awesome_model: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/soilSpoon/my_awesome_model/resolve/main/training_args.bin
- Command line
-
hf download hf://soilSpoon/my_awesome_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/soilSpoon/my_awesome_model/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 2390b5955a1c4b74a9c1593ef086c92fd846fdf706d104a1f8a319037fbf08ea
- Size of remote file:
- 4.73 kB
- SHA256:
- 40e98350d915f238673ead13c262109f640ada0a9ed31b068cc0284306b964c6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.