Instructions to use JavierIA/es-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JavierIA/es-en 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="JavierIA/es-en")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("JavierIA/es-en") model = AutoModelForSeq2SeqLM.from_pretrained("JavierIA/es-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from JavierIA/es-en: direct link, hf CLI and curl.
- Browser
- Download file 310 MB
-
https://huggingface.co/JavierIA/es-en/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://JavierIA/es-en/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/JavierIA/es-en/resolve/main/flax_model.msgpack
310 MB
- Xet hash:
- f7e178896747ee30398f3bab5c37a4dac1f202c8773cc69bc85fe94e6b55b99f
- Size of remote file:
- 310 MB
- SHA256:
- 14b5e0865da9b6764e18b637e7c7e34b04be927dc41a1ba97bff23962c2f5857
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