Instructions to use leejuhyoeng/lr5e-4_4warmup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leejuhyoeng/lr5e-4_4warmup with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="leejuhyoeng/lr5e-4_4warmup")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("leejuhyoeng/lr5e-4_4warmup") model = AutoModelForMultimodalLM.from_pretrained("leejuhyoeng/lr5e-4_4warmup", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd550451732dbf920c869313391960b20449d1be117cf9136627aee0bde7d2f9
- Size of remote file:
- 4.66 kB
- SHA256:
- 835faad6e7b683c72d8573fbe18b779ce5a7a8f09c1aabec8b33c31359bc738c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.