Instructions to use hf-tiny-model-private/tiny-random-FSMTForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-FSMTForConditionalGeneration with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-FSMTForConditionalGeneration") model = AutoModelForSeq2SeqLM.from_pretrained("hf-tiny-model-private/tiny-random-FSMTForConditionalGeneration", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-FSMTForConditionalGeneration: direct link, hf CLI and curl.
- Browser
- Download file 4.08 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-FSMTForConditionalGeneration/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-FSMTForConditionalGeneration/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-FSMTForConditionalGeneration/resolve/main/pytorch_model.bin
4.08 MB
- Xet hash:
- eb872b1336969876221f546c03047d7fac2ee303875531fe1ea5f6651c2c6716
- Size of remote file:
- 4.08 MB
- SHA256:
- 5c9fd2e45ad43486eacc93f223181b3edbc9539b7b75ca096c9ce37bb3921786
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