Instructions to use chakochen/mt5-destination-inference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chakochen/mt5-destination-inference 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="chakochen/mt5-destination-inference")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("chakochen/mt5-destination-inference") model = AutoModelForSeq2SeqLM.from_pretrained("chakochen/mt5-destination-inference", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from chakochen/mt5-destination-inference: direct link, hf CLI and curl.
- Browser
- Download file 4.22 kB
-
https://huggingface.co/chakochen/mt5-destination-inference/resolve/main/training_args.bin
- Command line
-
hf download hf://chakochen/mt5-destination-inference/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/chakochen/mt5-destination-inference/resolve/main/training_args.bin
4.22 kB
- Xet hash:
- eb3d06ea995f7703978170b860b64074f4dba083cbec503be1ddcc04c3f8a4e5
- Size of remote file:
- 4.22 kB
- SHA256:
- a82378c33090095e0eaad14f8dacfcb3bd6d641f1017372d8bb1b883b022f557
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