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 training_args.bin from thabat/Mymt5-small-test: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/thabat/Mymt5-small-test/resolve/main/training_args.bin
- Command line
-
hf download hf://thabat/Mymt5-small-test/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/thabat/Mymt5-small-test/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 8e72a49b9f02925225f462b4d6b76fe73a1b31085060c1834b6ef364c0d19715
- Size of remote file:
- 5.11 kB
- SHA256:
- 8772b9896f2523cb33bfcd71764a900d45e7bfed78f2098e2dc9ccd22314921e
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