Instructions to use PeterBanning71/long-t5-tfg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PeterBanning71/long-t5-tfg 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="PeterBanning71/long-t5-tfg")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("PeterBanning71/long-t5-tfg") model = AutoModelForSeq2SeqLM.from_pretrained("PeterBanning71/long-t5-tfg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ae04608c792c2e250edb464bb909f13ab284ecbfc32755d2a76306b8f1aa7fc
- Size of remote file:
- 990 MB
- SHA256:
- ea4c85e0b88a788b843c38a51532f921eaeab55495a673f87506cf0ad920d001
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