Instructions to use improz4/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use improz4/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="improz4/model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("improz4/model") model = AutoModelForMaskedLM.from_pretrained("improz4/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from improz4/model: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/improz4/model/resolve/8a2499e7ca88ea1df16bf245aaa2a6720ee02b83/training_args.bin
- Command line
-
hf download hf://improz4/model@8a2499e7ca88ea1df16bf245aaa2a6720ee02b83/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/improz4/model/resolve/8a2499e7ca88ea1df16bf245aaa2a6720ee02b83/training_args.bin
4.09 kB
- Xet hash:
- 7e5f27637c2b8d776df8e74dd4e578ee2894e059afc84d9608b3734b6ab0b89d
- Size of remote file:
- 4.09 kB
- SHA256:
- 6a9b530cd670b91f4852246d6304d8e334b8bc714cc4a2cb5f78d9d2089b61f9
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