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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("FAN-L/HM_model001") model = AutoModelForMaskedLM.from_pretrained("FAN-L/HM_model001", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from FAN-L/HM_model001: direct link, hf CLI and curl.
- Browser
- Download file 1.2 kB
-
https://huggingface.co/FAN-L/HM_model001/resolve/main/training_args.bin
- Command line
-
hf download hf://FAN-L/HM_model001/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/FAN-L/HM_model001/resolve/main/training_args.bin
1.2 kB
- Xet hash:
- c985b629f992081444fd7bc19ceb94a2cd94e7e31ddea2b2a529072e2beb80a8
- Size of remote file:
- 1.2 kB
- SHA256:
- 98f3d923781fb313a6d674b25d5e7154f49eb61777ca83189925468bb8a3cb4f
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