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 create_config.py from NbAiLabArchive/test_NCC_OSCAR_style_98w: direct link, hf CLI and curl.
- Browser
- Download file 163 Bytes
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https://huggingface.co/NbAiLabArchive/test_NCC_OSCAR_style_98w/resolve/ecaf4c0ee9782158366b5ba4caadf5cfc8c4a943/create_config.py
- Command line
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hf download hf://NbAiLabArchive/test_NCC_OSCAR_style_98w@ecaf4c0ee9782158366b5ba4caadf5cfc8c4a943/create_config.py
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curl -L -o create_config.py https://huggingface.co/NbAiLabArchive/test_NCC_OSCAR_style_98w/resolve/ecaf4c0ee9782158366b5ba4caadf5cfc8c4a943/create_config.py
163 Bytes
| from transformers import RobertaConfig | |
| model_dir = "./" # ${MODEL_DIR} | |
| config = RobertaConfig.from_pretrained("roberta-base") | |
| config.save_pretrained(model_dir) | |