Instructions to use choondrise/emolve with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use choondrise/emolve with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="choondrise/emolve")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("choondrise/emolve") model = AutoModelForSequenceClassification.from_pretrained("choondrise/emolve", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from choondrise/emolve: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/choondrise/emolve/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://choondrise/emolve/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/choondrise/emolve/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- faacb7afc8f6587d30246377d4efda9e15011277bcc1acdf2c6acf5015c7ef2d
- Size of remote file:
- 433 MB
- SHA256:
- cc9051ce8c9141070d45c264e0e9e9d387283356b7aa5fd2298bb8d249cebc68
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