Instructions to use ashdev01/my_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ashdev01/my_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ashdev01/my_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ashdev01/my_model") model = AutoModelForMaskedLM.from_pretrained("ashdev01/my_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ashdev01/my_model: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/ashdev01/my_model/resolve/main/training_args.bin
- Command line
-
hf download hf://ashdev01/my_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ashdev01/my_model/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 7468a00cddc0f956530fe27c0fabeda0c13375e82670f4cbfbe306f92008ee1c
- Size of remote file:
- 5.11 kB
- SHA256:
- 32b2dfaf681a342a5a5e67282ded80e5ad19ea0e44c3ace97986d04a79737280
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