Instructions to use adalbertojunior/rpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adalbertojunior/rpt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="adalbertojunior/rpt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("adalbertojunior/rpt") model = AutoModelForMaskedLM.from_pretrained("adalbertojunior/rpt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from adalbertojunior/rpt: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/adalbertojunior/rpt/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://adalbertojunior/rpt/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/adalbertojunior/rpt/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 18a6a4b1fcf386bda7f6e0c2405c2ddeeeed7d389fbca235363244ee3da44d5a
- Size of remote file:
- 499 MB
- SHA256:
- efbb23bfa6a87176cac90f08b837bc388c51b0b6c0289f2c1548b1184b167971
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