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 pipeline_verification/training_code_verification.json from bengoldberg0/granite-4.2-3b-phishing-url-qlora: direct link, hf CLI and curl.
- Browser
- Download file 347 Bytes
-
https://huggingface.co/bengoldberg0/granite-4.2-3b-phishing-url-qlora/resolve/main/pipeline_verification/training_code_verification.json
- Command line
-
hf download hf://bengoldberg0/granite-4.2-3b-phishing-url-qlora/pipeline_verification/training_code_verification.json
-
curl -L -o training_code_verification.json https://huggingface.co/bengoldberg0/granite-4.2-3b-phishing-url-qlora/resolve/main/pipeline_verification/training_code_verification.json
347 Bytes
| { | |
| "training_examples": 6000, | |
| "truncated_training_urls": 2, | |
| "longest_sequence": 504, | |
| "ordered_records_sha256": "52e26b2f20f38438b470056f7c5d794f9a844eb7bd6b51c5689a2eeb5a7402c8", | |
| "all_training_encodings_match_verified_release": true, | |
| "collator_checks_passed": true, | |
| "model_loaded": false, | |
| "refactored_training_run_executed": false | |
| } |