Instructions to use spursyy/mT5_multilingual_XLSum_rust with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spursyy/mT5_multilingual_XLSum_rust 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="spursyy/mT5_multilingual_XLSum_rust")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("spursyy/mT5_multilingual_XLSum_rust") model = AutoModelForSeq2SeqLM.from_pretrained("spursyy/mT5_multilingual_XLSum_rust", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from spursyy/mT5_multilingual_XLSum_rust: direct link, hf CLI and curl.
- Browser
- Download file 2.33 GB
-
https://huggingface.co/spursyy/mT5_multilingual_XLSum_rust/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://spursyy/mT5_multilingual_XLSum_rust/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/spursyy/mT5_multilingual_XLSum_rust/resolve/main/pytorch_model.bin
2.33 GB
- Xet hash:
- 28677e666c17134b1ae5c9561a011f0b04d0d35d7908cb8414e10fdbda2ac7b5
- Size of remote file:
- 2.33 GB
- SHA256:
- 1899a041aceedfd0c9c67e87f2597bc597ce6f4c1f21b5d35a6325322608a898
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