Instructions to use junkmind/my_awesome with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junkmind/my_awesome with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="junkmind/my_awesome")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("junkmind/my_awesome") model = AutoModelForSequenceClassification.from_pretrained("junkmind/my_awesome", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from junkmind/my_awesome: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/junkmind/my_awesome/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://junkmind/my_awesome/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/junkmind/my_awesome/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 3e085381affa804014fd840746af7dbcf6cae2a648b627af15aba76efe4cdca7
- Size of remote file:
- 268 MB
- SHA256:
- a4dd0d5ec8e764e7282011dcc783dfa25d266d1a1ec4aadd222a76a60be5a675
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.