Instructions to use facebook/mms-1b-fl102 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-1b-fl102 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/mms-1b-fl102")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("facebook/mms-1b-fl102") model = AutoModelForCTC.from_pretrained("facebook/mms-1b-fl102", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download adapter.fin.bin from facebook/mms-1b-fl102: direct link, hf CLI and curl.
- Browser
- Download file 9.06 MB
-
https://huggingface.co/facebook/mms-1b-fl102/resolve/main/adapter.fin.bin
- Command line
-
hf download hf://facebook/mms-1b-fl102/adapter.fin.bin
-
curl -L -o adapter.fin.bin https://huggingface.co/facebook/mms-1b-fl102/resolve/main/adapter.fin.bin
9.06 MB
- Xet hash:
- e0e3ee6f9d5440c28e2a401e3b550fed988388999c9370bd7a63f33a7ca4184f
- Size of remote file:
- 9.06 MB
- SHA256:
- 936a75ee315d1a368a7c2306d1a52a5e920435055b4f50dcfb92bd993f3e2b72
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.