Instructions to use hztang/BART-large-base-custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hztang/BART-large-base-custom with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hztang/BART-large-base-custom") model = AutoModelForSeq2SeqLM.from_pretrained("hztang/BART-large-base-custom", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hztang/BART-large-base-custom: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/hztang/BART-large-base-custom/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hztang/BART-large-base-custom/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hztang/BART-large-base-custom/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- d6d65e463eec6daa3facbb3c39bd9510e32f00b8d86cd655abdab9e9dc744193
- Size of remote file:
- 1.63 GB
- SHA256:
- f4247d1a83109dad43e242765693e7b4ec9301cbb5021c7b9a4d430bddbd3f27
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