Instructions to use NbAiLabArchive/test_w5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/test_w5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLabArchive/test_w5")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLabArchive/test_w5") model = AutoModelForMaskedLM.from_pretrained("NbAiLabArchive/test_w5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLabArchive/test_w5: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/NbAiLabArchive/test_w5/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLabArchive/test_w5/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLabArchive/test_w5/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 800c5794b2c05e284ad046c4273bebcdb06ea939aa4d8267ecebd44a77ca017c
- Size of remote file:
- 499 MB
- SHA256:
- 0c0ef99d6b6e1a47ad00474100dd3a3c9c17c8cec41fde77b1252184054f2168
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.