Instructions to use sam2ai/nllb-3.3b-hindi-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sam2ai/nllb-3.3b-hindi-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-3.3B") model = PeftModel.from_pretrained(base_model, "sam2ai/nllb-3.3b-hindi-lora") - Transformers
How to use sam2ai/nllb-3.3b-hindi-lora with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sam2ai/nllb-3.3b-hindi-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from sam2ai/nllb-3.3b-hindi-lora: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/sam2ai/nllb-3.3b-hindi-lora/resolve/main/tokenizer.json
- Command line
-
hf download hf://sam2ai/nllb-3.3b-hindi-lora/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/sam2ai/nllb-3.3b-hindi-lora/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- 706d868fdd91889c442341097ece564a9f138e58baa7d2c1691422366c6cebd0
- Size of remote file:
- 32.2 MB
- SHA256:
- 1480ef81b8195decc425cba547a80635c1ad9409d15eb250fa06485f415a6fc7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.