Instructions to use Ash0k0723/mms-1b-npi-finetuned-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ash0k0723/mms-1b-npi-finetuned-lora with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("facebook/mms-1b-all") model = PeftModel.from_pretrained(base_model, "Ash0k0723/mms-1b-npi-finetuned-lora") - Transformers
How to use Ash0k0723/mms-1b-npi-finetuned-lora with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Ash0k0723/mms-1b-npi-finetuned-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Ash0k0723/mms-1b-npi-finetuned-lora: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/Ash0k0723/mms-1b-npi-finetuned-lora/resolve/main/training_args.bin
- Command line
-
hf download hf://Ash0k0723/mms-1b-npi-finetuned-lora/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ash0k0723/mms-1b-npi-finetuned-lora/resolve/main/training_args.bin
5.84 kB
- Xet hash:
- ab851ef5904d6ea4c616cb3161d44629adbe5527aeeed425f635708d97ea6585
- Size of remote file:
- 5.84 kB
- SHA256:
- 0077b55adcb1bca33fbcf2073f40af30f442e64361cee6596eb1b05b2897ff82
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