Text Classification
PEFT
Safetensors
Transformers
English
lora
audio-question-answering
correctness-assessment
orca
Instructions to use BUT-FIT/orca-llama-3.2-3b-it-multinomial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use BUT-FIT/orca-llama-3.2-3b-it-multinomial with PEFT:
Task type is invalid.
- Transformers
How to use BUT-FIT/orca-llama-3.2-3b-it-multinomial with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BUT-FIT/orca-llama-3.2-3b-it-multinomial")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BUT-FIT/orca-llama-3.2-3b-it-multinomial", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e56cad9ac2a38f4b7de211953881b613c19e51838c05adf8f7984c139c2e8f5
- Size of remote file:
- 17.2 MB
- SHA256:
- 6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.