Instructions to use mbouilloud/0.57 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbouilloud/0.57 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mbouilloud/0.57")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mbouilloud/0.57") model = AutoModelForSequenceClassification.from_pretrained("mbouilloud/0.57", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mbouilloud/0.57: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/mbouilloud/0.57/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mbouilloud/0.57/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mbouilloud/0.57/resolve/main/pytorch_model.bin
557 MB
- Xet hash:
- faf8fc992e6d8dba91768b95793094084bcba8610b3d39d051078d2036eae546
- Size of remote file:
- 557 MB
- SHA256:
- d776b2d485c9912776ed2031067c9c85ed969a8229f771573acf723a017ca953
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.