Instructions to use mj124/use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mj124/use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="mj124/use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("mj124/use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("mj124/use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mj124/use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/mj124/use_data_finetuning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mj124/use_data_finetuning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mj124/use_data_finetuning/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- bc481f26dc1cc05f8c9f424cdf1a928cad91f67703bdbe55ffe57964bb410c09
- Size of remote file:
- 167 MB
- SHA256:
- f8c2172c7fffe6137f649bb2274b68d0e18a123d43a111a345218924ffaa934c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.