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 4.54 kB
-
https://huggingface.co/improz4/model/resolve/c5137cedc97276009f8abcb1d8d73c8c19afabcc/training_args.bin
- Command line
-
hf download hf://improz4/model@c5137cedc97276009f8abcb1d8d73c8c19afabcc/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/improz4/model/resolve/c5137cedc97276009f8abcb1d8d73c8c19afabcc/training_args.bin
4.54 kB
- Xet hash:
- 368ca193af4d8656f19024a2acb18f4dc65d23f11f43e7b4f7dbc0d42f174ee1
- Size of remote file:
- 4.54 kB
- SHA256:
- 87de890b419738af5b56df084067ec474d8ce8207358a8f12025fc2067d3b943
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