Instructions to use facebook/mms-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-1b with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("facebook/mms-1b") model = AutoModelForPreTraining.from_pretrained("facebook/mms-1b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/mms-1b: direct link, hf CLI and curl.
- Browser
- Download file 3.86 GB
-
https://huggingface.co/facebook/mms-1b/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/mms-1b/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/mms-1b/resolve/main/pytorch_model.bin
3.86 GB
- Xet hash:
- 88ab32b60df178d18c5e39cfbe318c071329c0315f442500522042b86c7fa41c
- Size of remote file:
- 3.86 GB
- SHA256:
- ddf6980ef183118e5873cfb4c4789a90386b87a6fe3fafa8a08a822f557d68f7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.