Instructions to use shed-e/Summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shed-e/Summary 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="shed-e/Summary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("shed-e/Summary") model = AutoModelForSeq2SeqLM.from_pretrained("shed-e/Summary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from shed-e/Summary: direct link, hf CLI and curl.
- Browser
- Download file 3.5 kB
-
https://huggingface.co/shed-e/Summary/resolve/refs%2Fpr%2F2/training_args.bin
- Command line
-
hf download hf://shed-e/Summary@refs/pr/2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/shed-e/Summary/resolve/refs%2Fpr%2F2/training_args.bin
3.5 kB
- Xet hash:
- 91ba9e84205bbe267ab628e134ffbd4b88690ccafa81fd252c5507a46df4a5f1
- Size of remote file:
- 3.5 kB
- SHA256:
- 5d9c2580f57328d7402bfdaa1e8e92fbf60214bd5870bff00fddccebeac7c37d
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