Instructions to use NbAiLabArchive/test_NCC_OSCAR_style_98w with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/test_NCC_OSCAR_style_98w with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLabArchive/test_NCC_OSCAR_style_98w")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLabArchive/test_NCC_OSCAR_style_98w") model = AutoModelForMaskedLM.from_pretrained("NbAiLabArchive/test_NCC_OSCAR_style_98w", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLabArchive/test_NCC_OSCAR_style_98w: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/NbAiLabArchive/test_NCC_OSCAR_style_98w/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLabArchive/test_NCC_OSCAR_style_98w/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLabArchive/test_NCC_OSCAR_style_98w/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- a299a938003bda086e7382b14c984dabda64044fe4a19eecc422e5ed5cb57735
- Size of remote file:
- 499 MB
- SHA256:
- 14bf609c747e6a2c7f052ea5a27a70190ec5a3f0f91c43ff4a88931b95d02aaf
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