Instructions to use thabat/Mymt5-small-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thabat/Mymt5-small-test 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="thabat/Mymt5-small-test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("thabat/Mymt5-small-test") model = AutoModelForSeq2SeqLM.from_pretrained("thabat/Mymt5-small-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from thabat/Mymt5-small-test: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/thabat/Mymt5-small-test/resolve/main/tokenizer.json
- Command line
-
hf download hf://thabat/Mymt5-small-test/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/thabat/Mymt5-small-test/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 49bce0d71ebca7b5fd1c41036515153a53c7772290a2212b352270570ada57ae
- Size of remote file:
- 16.3 MB
- SHA256:
- ddb1b00035edffde435e24e69011b7f552691405237455cb7c3a59c890682807
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