Instructions to use kamel-usp/jbcs2025_phi-4-phi4_classification_lora-C4-full_context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kamel-usp/jbcs2025_phi-4-phi4_classification_lora-C4-full_context with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("microsoft/phi-4") model = PeftModel.from_pretrained(base_model, "kamel-usp/jbcs2025_phi-4-phi4_classification_lora-C4-full_context") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kamel-usp/jbcs2025_phi-4-phi4_classification_lora-C4-full_context: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/kamel-usp/jbcs2025_phi-4-phi4_classification_lora-C4-full_context/resolve/main/training_args.bin
- Command line
-
hf download hf://kamel-usp/jbcs2025_phi-4-phi4_classification_lora-C4-full_context/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kamel-usp/jbcs2025_phi-4-phi4_classification_lora-C4-full_context/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- 39ea9e42bef13d3c4df740d36f04a6356912293c0bbbeed9c519a833462f7405
- Size of remote file:
- 5.78 kB
- SHA256:
- 8a3ebd354f4e984559ee5c5c74d92ad12a7713a500a4b49ca529202abe3f4102
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.