Instructions to use ax13/summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ax13/summarizer 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="ax13/summarizer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ax13/summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("ax13/summarizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ax13/summarizer: direct link, hf CLI and curl.
- Browser
- Download file 3.71 kB
-
https://huggingface.co/ax13/summarizer/resolve/main/training_args.bin
- Command line
-
hf download hf://ax13/summarizer/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ax13/summarizer/resolve/main/training_args.bin
3.71 kB
- Xet hash:
- e7a83fd37ef4679761bdb983d2635fd271457d07f3818ff94a558fa8e6e47150
- Size of remote file:
- 3.71 kB
- SHA256:
- 1536079363325baa4f251b8b42e93798dca09fa57e5302b3c32f6d68ac818b7f
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