Instructions to use chenhg8680/mt5-sum-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chenhg8680/mt5-sum-v1 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="chenhg8680/mt5-sum-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("chenhg8680/mt5-sum-v1") model = AutoModelForSeq2SeqLM.from_pretrained("chenhg8680/mt5-sum-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from chenhg8680/mt5-sum-v1: direct link, hf CLI and curl.
- Browser
- Download file 4.92 GB
-
https://huggingface.co/chenhg8680/mt5-sum-v1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://chenhg8680/mt5-sum-v1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/chenhg8680/mt5-sum-v1/resolve/main/pytorch_model.bin
4.92 GB
- Xet hash:
- 6b498fa3db269c2c6d97994118d1da08b94f4f052a576fc13788e8bec8e8f8cd
- Size of remote file:
- 4.92 GB
- SHA256:
- 141c78494e15ce3b10fa41562c9dc8929159e1b98c4c47fb6e07c16bd09a0a4b
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