Instructions to use hurtmongoose/phi2-lora-malicious-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use hurtmongoose/phi2-lora-malicious-classifier with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("microsoft/phi-2") model = PeftModel.from_pretrained(base_model, "hurtmongoose/phi2-lora-malicious-classifier") - Transformers
How to use hurtmongoose/phi2-lora-malicious-classifier with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hurtmongoose/phi2-lora-malicious-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hurtmongoose/phi2-lora-malicious-classifier: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/hurtmongoose/phi2-lora-malicious-classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://hurtmongoose/phi2-lora-malicious-classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hurtmongoose/phi2-lora-malicious-classifier/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 9da714c357622f8a14aa1195b2dc60c36a1a74deb7ff49e2a13ae1ddb3420dc9
- Size of remote file:
- 5.37 kB
- SHA256:
- 6e96e6842f71bf4504acb5a48318dfe3ef268b8328be1e10b44699bb29864e0c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.