Instructions to use tringuyen1032/summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tringuyen1032/summarization 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="tringuyen1032/summarization")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("tringuyen1032/summarization") model = AutoModelForSeq2SeqLM.from_pretrained("tringuyen1032/summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tringuyen1032/summarization: direct link, hf CLI and curl.
- Browser
- Download file 242 MB
-
https://huggingface.co/tringuyen1032/summarization/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tringuyen1032/summarization/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tringuyen1032/summarization/resolve/main/pytorch_model.bin
242 MB
- Xet hash:
- 310d40fde0c7a92d2d7cab98c97bc457a1a44124f2744dd919e6d90491a8367d
- Size of remote file:
- 242 MB
- SHA256:
- 6433a0e06de851b47a4e3cc685a8099f57f733d2db721016be574595eb5d10db
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