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 tokenizer.json from chakochen/mt5-destination-inference: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/chakochen/mt5-destination-inference/resolve/main/tokenizer.json
- Command line
-
hf download hf://chakochen/mt5-destination-inference/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/chakochen/mt5-destination-inference/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- cd574c811387f7d47b9e9dab6cf64fd47f54dfa0d1c8f4595c9c5c314f2ba146
- Size of remote file:
- 16.3 MB
- SHA256:
- f78e31032f75636d8e96b28eaf4c134b85c549906025db114eeb41ecb311d580
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