Instructions to use andres-gv/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andres-gv/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="andres-gv/tmp_trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("andres-gv/tmp_trainer") model = AutoModelForSequenceClassification.from_pretrained("andres-gv/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from andres-gv/tmp_trainer: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/andres-gv/tmp_trainer/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://andres-gv/tmp_trainer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/andres-gv/tmp_trainer/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- b4cbfac9f1c5619523216c4f48e32dfcb9b3fba2622da7fe08bb5ab6654efff7
- Size of remote file:
- 268 MB
- SHA256:
- 4f8c0782538e5e5d0fd52f77d9b0708731cf0183a8699731e38b0e239d39df50
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