Instructions to use Bakobiibizo/nimble-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bakobiibizo/nimble-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Bakobiibizo/nimble-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Bakobiibizo/nimble-bert") model = AutoModelForSequenceClassification.from_pretrained("Bakobiibizo/nimble-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Bakobiibizo/nimble-bert: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Bakobiibizo/nimble-bert/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Bakobiibizo/nimble-bert/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Bakobiibizo/nimble-bert/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- a2405d939b6b37835077c7b62c3a106dc17565a1236ce95702f12f12731fa4c0
- Size of remote file:
- 438 MB
- SHA256:
- 80c93148809240ce872694c420e0b28a6d5048518edd50f91b9ac4f2825be5d5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.