Instructions to use FAN-L/HM_model001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FAN-L/HM_model001 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="FAN-L/HM_model001")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("FAN-L/HM_model001") model = AutoModelForMaskedLM.from_pretrained("FAN-L/HM_model001") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ad034b70d4236aebbb96081f81091e127538cdc2da00140718f89396e104f0b
- Size of remote file:
- 438 MB
- SHA256:
- 662f0a69848866d00d6d9253435b9f82cadbee190b38b0d836f951d842cf4bda
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