Instructions to use hf-internal-testing/tiny-random-NllbMoeForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-NllbMoeForConditionalGeneration with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("hf-internal-testing/tiny-random-NllbMoeForConditionalGeneration", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-NllbMoeForConditionalGeneration: direct link, hf CLI and curl.
- Browser
- Download file 234 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-NllbMoeForConditionalGeneration/resolve/refs%2Fpr%2F10/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-NllbMoeForConditionalGeneration@refs/pr/10/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-NllbMoeForConditionalGeneration/resolve/refs%2Fpr%2F10/pytorch_model.bin
234 kB
- Xet hash:
- 2c35effcf06836d25e6b09975675c4ddbfc2d392279aa927057b82eeda5c0190
- Size of remote file:
- 234 kB
- SHA256:
- 828580e1f2a31f711e5ec7a3e9b20450d3144fb090b5416622baf9369957df4b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.