Instructions to use Lollover/trainer_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lollover/trainer_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Lollover/trainer_output")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Lollover/trainer_output") model = AutoModelForObjectDetection.from_pretrained("Lollover/trainer_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Lollover/trainer_output: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/Lollover/trainer_output/resolve/main/training_args.bin
- Command line
-
hf download hf://Lollover/trainer_output/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Lollover/trainer_output/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- 3309dad4e996a4f7e1f92c03134abb4b4bbf1c681573e5098449699987205097
- Size of remote file:
- 5.2 kB
- SHA256:
- 15fde9e8c3e4fbe956644487475d5f0443200adc91cbbb8530cb50b61f258d3d
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