Instructions to use d0r1h/led-base-ilc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d0r1h/led-base-ilc with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="d0r1h/led-base-ilc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("d0r1h/led-base-ilc") model = AutoModelForSeq2SeqLM.from_pretrained("d0r1h/led-base-ilc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from d0r1h/led-base-ilc: direct link, hf CLI and curl.
- Browser
- Download file 648 MB
-
https://huggingface.co/d0r1h/led-base-ilc/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://d0r1h/led-base-ilc/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/d0r1h/led-base-ilc/resolve/main/pytorch_model.bin
648 MB
- Xet hash:
- 8adade107fa03dee5fb0a8acb96baef763c73025446171ccc387e1dbf8fc71c2
- Size of remote file:
- 648 MB
- SHA256:
- db871cd68ae35d0e6ec5ad4fae25b5b847effc8e6660cd371935d52df0019136
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