Instructions to use ryul99/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ryul99/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="ryul99/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("ryul99/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("ryul99/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ryul99/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/ryul99/cppe5_use_data_finetuning/resolve/34d6c8a52be1a61bdc5491b400a55ab2450cbfee/training_args.bin
- Command line
-
hf download hf://ryul99/cppe5_use_data_finetuning@34d6c8a52be1a61bdc5491b400a55ab2450cbfee/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ryul99/cppe5_use_data_finetuning/resolve/34d6c8a52be1a61bdc5491b400a55ab2450cbfee/training_args.bin
4.54 kB
- Xet hash:
- 4c2a3e6b94814f7f1bcbc1f06c3038730d98879c639daaf1f3d1300e075a5bd2
- Size of remote file:
- 4.54 kB
- SHA256:
- 9f312542820d847995f8439bb4e557b5afed5ccdd67a0a29479d050f5887bd2c
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