Instructions to use bengoldberg0/granite-4.2-3b-phishing-url-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bengoldberg0/granite-4.2-3b-phishing-url-qlora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-4.2-3b") model = PeftModel.from_pretrained(base_model, "bengoldberg0/granite-4.2-3b-phishing-url-qlora") - Notebooks
- Google Colab
- Kaggle
Download results/benchmark_comparison.png from bengoldberg0/granite-4.2-3b-phishing-url-qlora: direct link, hf CLI and curl.
- Browser
- Download file 74.3 kB
-
https://huggingface.co/bengoldberg0/granite-4.2-3b-phishing-url-qlora/resolve/main/results/benchmark_comparison.png
- Command line
-
hf download hf://bengoldberg0/granite-4.2-3b-phishing-url-qlora/results/benchmark_comparison.png
-
curl -L -o benchmark_comparison.png https://huggingface.co/bengoldberg0/granite-4.2-3b-phishing-url-qlora/resolve/main/results/benchmark_comparison.png
74.3 kB

- Xet hash:
- 6afaf406eed41c013fb090af9fbb6062fa7cabc1d86c1cc16518fccfea1fbaf9
- Size of remote file:
- 74.3 kB
- SHA256:
- 4d21939aa9c51e3d2b0a1e371a13416aa060055abbbb284a8e3c3a4d5f807bbb
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