Instructions to use krm/BARTkrame-abstract-mT5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krm/BARTkrame-abstract-mT5 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="krm/BARTkrame-abstract-mT5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("krm/BARTkrame-abstract-mT5") model = AutoModelForSeq2SeqLM.from_pretrained("krm/BARTkrame-abstract-mT5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from krm/BARTkrame-abstract-mT5: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/krm/BARTkrame-abstract-mT5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://krm/BARTkrame-abstract-mT5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/krm/BARTkrame-abstract-mT5/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- 19a5541064500e6e36eb261f6dc2127c0194204d3cd22c7aad8c0e51a6736b8c
- Size of remote file:
- 1.2 GB
- SHA256:
- 290304334862320311e5bbfc605ee94abf5c554c91645fc98608d3d1b731da70
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