Instructions to use NbAiLabArchive/test999 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/test999 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLabArchive/test999")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLabArchive/test999") model = AutoModelForMaskedLM.from_pretrained("NbAiLabArchive/test999", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLabArchive/test999: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/NbAiLabArchive/test999/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLabArchive/test999/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLabArchive/test999/resolve/main/flax_model.msgpack
1.11 GB
- Xet hash:
- 3a1ebe3f01ead41714e1437c0d558fdc947b329de53eb85c96a0aa1f6b514d06
- Size of remote file:
- 1.11 GB
- SHA256:
- 4effce844c484aa584fd9238ee5ccde1bdfcd97e94bc3501c4c527f6dcad8e67
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.