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
Commit History
-
Update README.md
08a1d52 -
Update README.md
5ed0b20
-
Update README.md
259741b -
Update README.md
6a62f6e -
Upload scaler.pt with git-lfs
1536934 -
Upload scheduler.pt with git-lfs
224e952 -
Upload special_tokens_map.json
ea9215b -
Upload config.json
d2ae7e2 -
Upload tokenizer_config.json
c1678d6 -
Upload trainer_state.json
9d4f5cd -
Upload training_args.bin with git-lfs
ed3f348 -
Upload rng_state.pth with git-lfs
fdc17ed -
Upload merges.txt
8f71aee -
Upload vocab.json
0d74a76 -
Upload tokenizer.json
ac71da7 -
Upload pytorch_model.bin with git-lfs
6f12a8b -
Upload optimizer.pt with git-lfs
8880097 -
initial commit
d3b95a8