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 pytorch_model.bin from chakochen/mt5-destination-inference: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/chakochen/mt5-destination-inference/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://chakochen/mt5-destination-inference/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/chakochen/mt5-destination-inference/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- 6524448535571666395c8d4506c407ddaf764ed9bf0a01b3050f8589911a984d
- Size of remote file:
- 1.2 GB
- SHA256:
- a8bd5ac89e4652b9123080bce3678bfb68914a8a5b786b353de0f244792ebe70
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