Instructions to use bitmorse/kickstarter-distilbert-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bitmorse/kickstarter-distilbert-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bitmorse/kickstarter-distilbert-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bitmorse/kickstarter-distilbert-model") model = AutoModel.from_pretrained("bitmorse/kickstarter-distilbert-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bitmorse/kickstarter-distilbert-model: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/bitmorse/kickstarter-distilbert-model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://bitmorse/kickstarter-distilbert-model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bitmorse/kickstarter-distilbert-model/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- 8645bd8c4a9f892628eba1ba66519a5bc8eedfce46c45fd12f0e45bcb2c269c5
- Size of remote file:
- 265 MB
- SHA256:
- 3901f44516fc62ca80640a14f53b2f6e572a61f53e641e1bf09c44aef12a8711
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.