Instructions to use Helsinki-NLP/opus-mt-en-hi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-en-hi with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-hi") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-hi", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Helsinki-NLP/opus-mt-en-hi: direct link, hf CLI and curl.
- Browser
- Download file 306 MB
-
https://huggingface.co/Helsinki-NLP/opus-mt-en-hi/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Helsinki-NLP/opus-mt-en-hi/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Helsinki-NLP/opus-mt-en-hi/resolve/main/pytorch_model.bin
306 MB
- Xet hash:
- 159a49539201ec8c213fa464bf30443310128e13bb3399f01fe69ac292f94b48
- Size of remote file:
- 306 MB
- SHA256:
- b07f16822d80aabd93e5ae05ac7e0dbda18989ecb3942e926214ef13ddb593dd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.