Instructions to use choondrise/emolve_basic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use choondrise/emolve_basic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="choondrise/emolve_basic")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("choondrise/emolve_basic") model = AutoModelForSequenceClassification.from_pretrained("choondrise/emolve_basic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from choondrise/emolve_basic: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/choondrise/emolve_basic/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://choondrise/emolve_basic/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/choondrise/emolve_basic/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 9dbc4045f84eb8992ccb065fbb27af44f8aae6c723ca2c93fac523eb34d90b57
- Size of remote file:
- 433 MB
- SHA256:
- 4f96f6013bdedf6e14ddfd56b7ba7be88f63ef1308ae939cdcef860dfedd2176
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.