Instructions to use wanglab/task-b-led-large-16384-pubmed-run-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wanglab/task-b-led-large-16384-pubmed-run-3 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("wanglab/task-b-led-large-16384-pubmed-run-3") model = AutoModelForSeq2SeqLM.from_pretrained("wanglab/task-b-led-large-16384-pubmed-run-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from wanglab/task-b-led-large-16384-pubmed-run-3: direct link, hf CLI and curl.
- Browser
- Download file 1.84 GB
-
https://huggingface.co/wanglab/task-b-led-large-16384-pubmed-run-3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://wanglab/task-b-led-large-16384-pubmed-run-3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/wanglab/task-b-led-large-16384-pubmed-run-3/resolve/main/pytorch_model.bin
1.84 GB
- Xet hash:
- 9fc435a629223f7b7c926dab5f0df3076ffabb68f934e0aaa647e8532605c5f6
- Size of remote file:
- 1.84 GB
- SHA256:
- 41c65d57fbc852ba7faf27e18120f471947e175a2a19190c721d4a40c3f68606
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