Instructions to use saivineetha/nllb_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use saivineetha/nllb_lora with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M") model = PeftModel.from_pretrained(base_model, "saivineetha/nllb_lora") - Transformers
How to use saivineetha/nllb_lora with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("saivineetha/nllb_lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from saivineetha/nllb_lora: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/saivineetha/nllb_lora/resolve/main/tokenizer.json
- Command line
-
hf download hf://saivineetha/nllb_lora/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/saivineetha/nllb_lora/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- 3bd76e85456a7d2b81d61a7a16c2a5e0d184ef6d8d1bdff5e41df3fce8a1a75f
- Size of remote file:
- 32.2 MB
- SHA256:
- cddfe6923cc89e9f0cbde33409e8e792b157759023126f48f5b87a408d486fc4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.