Instructions to use dtorber/NAS-bilingue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtorber/NAS-bilingue with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="dtorber/NAS-bilingue")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dtorber/NAS-bilingue") model = AutoModelForSeq2SeqLM.from_pretrained("dtorber/NAS-bilingue", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dtorber/NAS-bilingue: direct link, hf CLI and curl.
- Browser
- Download file 1.66 GB
-
https://huggingface.co/dtorber/NAS-bilingue/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dtorber/NAS-bilingue/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dtorber/NAS-bilingue/resolve/main/pytorch_model.bin
1.66 GB
- Xet hash:
- 4835ae65d484ec41a797da5919b12cb6fd05f954545f1d93da7ea365712a0b65
- Size of remote file:
- 1.66 GB
- SHA256:
- e504b2d3714b02d3f846846576f375f33645552bb13b9baa73f237191d4c0aff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.