Instructions to use erbacher/flanwithoutpassage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use erbacher/flanwithoutpassage with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("erbacher/flanwithoutpassage") model = AutoModelForSeq2SeqLM.from_pretrained("erbacher/flanwithoutpassage", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from erbacher/flanwithoutpassage: direct link, hf CLI and curl.
- Browser
- Download file 3.13 GB
-
https://huggingface.co/erbacher/flanwithoutpassage/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://erbacher/flanwithoutpassage/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/erbacher/flanwithoutpassage/resolve/main/pytorch_model.bin
3.13 GB
- Xet hash:
- ef79f273cc9aae97c6b091124fb00ce5f4cddec610a50211cbc3064e0b3d182c
- Size of remote file:
- 3.13 GB
- SHA256:
- 09e9fc2ed38710ceb7bceb03b469789a63cba374cc2bc22d61c7aa11c42c15b9
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