Instructions to use hurtmongoose/tinyllama-lora-malicious-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use hurtmongoose/tinyllama-lora-malicious-classifier with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("TinyLlama/TinyLlama_v1.1") model = PeftModel.from_pretrained(base_model, "hurtmongoose/tinyllama-lora-malicious-classifier") - Transformers
How to use hurtmongoose/tinyllama-lora-malicious-classifier with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hurtmongoose/tinyllama-lora-malicious-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hurtmongoose/tinyllama-lora-malicious-classifier: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/hurtmongoose/tinyllama-lora-malicious-classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://hurtmongoose/tinyllama-lora-malicious-classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hurtmongoose/tinyllama-lora-malicious-classifier/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- ca9dfbf487e0ae844eeb7f0835fe1a33295a9c9dc08c58fe2e1ff200471e8a88
- Size of remote file:
- 5.37 kB
- SHA256:
- 276dccb410726ee4edf44fd8c712bea3319cdfd108f505a8451f1fc7b3c7dc6a
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