Instructions to use Pommu/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pommu/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Pommu/model_output")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Pommu/model_output") model = AutoModelForSequenceClassification.from_pretrained("Pommu/model_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Pommu/model_output: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/Pommu/model_output/resolve/main/training_args.bin
- Command line
-
hf download hf://Pommu/model_output/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Pommu/model_output/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- 670c12685c89fbb05fab89edcbef835bb9a4dca6191789da9108d1dce39c5301
- Size of remote file:
- 5.2 kB
- SHA256:
- c9f7729dfcbbe71f621fa0406722b5af16eda8a9e54e932d598b8b7c83631f72
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