Instructions to use Deepakvictor/tan-ta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Deepakvictor/tan-ta 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="Deepakvictor/tan-ta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Deepakvictor/tan-ta") model = AutoModelForSeq2SeqLM.from_pretrained("Deepakvictor/tan-ta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Deepakvictor/tan-ta: direct link, hf CLI and curl.
- Browser
- Download file 1.94 GB
-
https://huggingface.co/Deepakvictor/tan-ta/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://Deepakvictor/tan-ta@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Deepakvictor/tan-ta/resolve/refs%2Fpr%2F2/pytorch_model.bin
1.94 GB
- Xet hash:
- b1d1a4e1660c7079eca392eb671a04a231b0c50c5942bb12f3e1e1fc80df8bb6
- Size of remote file:
- 1.94 GB
- SHA256:
- 42a10486d10910ae506a78a20f639545a2ea4e148b254929cc5f720ed4d42c52
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