Instructions to use damgomz/fp_bs8_lr5_x12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use damgomz/fp_bs8_lr5_x12 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="damgomz/fp_bs8_lr5_x12", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("damgomz/fp_bs8_lr5_x12") model = AutoModelForMaskedLM.from_pretrained("damgomz/fp_bs8_lr5_x12", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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