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 training_args.bin from d0r1h/led-base-ilc: direct link, hf CLI and curl.
- Browser
- Download file 3.12 kB
-
https://huggingface.co/d0r1h/led-base-ilc/resolve/main/training_args.bin
- Command line
-
hf download hf://d0r1h/led-base-ilc/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/d0r1h/led-base-ilc/resolve/main/training_args.bin
3.12 kB
- Xet hash:
- 270d3404ace260727c23bb4e7ed4dd9e3e311960e9db340212a7cef6777cc183
- Size of remote file:
- 3.12 kB
- SHA256:
- f949c5dcd9e8116da9c0d7815d4eb8974011b2ceb455c3ab08a88df0dcee73b9
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