Instructions to use firebolt/llama_or_what2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use firebolt/llama_or_what2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="firebolt/llama_or_what2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("firebolt/llama_or_what2") model = AutoModelForImageClassification.from_pretrained("firebolt/llama_or_what2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34340c359148fe7e6013073718055b83130f0ff20a6c701439608313ac5e8362
- Size of remote file:
- 343 MB
- SHA256:
- 3fbd6d7de14e917b01a0c6224d68fddd67461fb6f54a7e1e4e8cb34f217d1638
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.