Instructions to use rifkat/robert_BPE_zinc100k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rifkat/robert_BPE_zinc100k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="rifkat/robert_BPE_zinc100k")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("rifkat/robert_BPE_zinc100k") model = AutoModelForMaskedLM.from_pretrained("rifkat/robert_BPE_zinc100k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rifkat/robert_BPE_zinc100k: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/rifkat/robert_BPE_zinc100k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rifkat/robert_BPE_zinc100k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rifkat/robert_BPE_zinc100k/resolve/main/pytorch_model.bin
334 MB
- Xet hash:
- 6f4137ddc5895da8148ce77a0beff977df9f5e6b6e694a8f385c5725eabf536f
- Size of remote file:
- 334 MB
- SHA256:
- 57f5e4275f5d00f3ba4fbee60d7bb14494038f03abe7d879c3de9c9c77c6c457
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