Instructions to use SBMI/pert_gpt_63M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SBMI/pert_gpt_63M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SBMI/pert_gpt_63M")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SBMI/pert_gpt_63M") model = AutoModelForMaskedLM.from_pretrained("SBMI/pert_gpt_63M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from SBMI/pert_gpt_63M: direct link, hf CLI and curl.
- Browser
- Download file 5.82 kB
-
https://huggingface.co/SBMI/pert_gpt_63M/resolve/main/training_args.bin
- Command line
-
hf download hf://SBMI/pert_gpt_63M/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/SBMI/pert_gpt_63M/resolve/main/training_args.bin
5.82 kB
- Xet hash:
- acd0a064a739c303356e4516df7ca599e4b1b8353394e3c487c5be56ed8f2b8f
- Size of remote file:
- 5.82 kB
- SHA256:
- 8a119881ffce1c657f8c0491b531305d7c7ce7b12c0665197b11e4e8650991d0
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