Instructions to use HEART77/VIGIL-Qwen3.5-4B-L3-Replication with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use HEART77/VIGIL-Qwen3.5-4B-L3-Replication with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B") model = PeftModel.from_pretrained(base_model, "HEART77/VIGIL-Qwen3.5-4B-L3-Replication") - Notebooks
- Google Colab
- Kaggle
VIGIL Qwen3.5-4B L3 Replication LoRA
Cross-scale replication of the VIGIL L3 wrong-image rejection-ranking method. It was trained for one epoch on the same strict 1,000 paired groups and uses a neutral prompt.
Selected-checkpoint results: original accuracy 70.57%, CVE 70.57%, wrong-image LPE 14.89%, OAR 1.06%, fixed-300 agreement 71.33%, and count agreement 64.24%. The ranking mechanism transfers to 4B, but this checkpoint does not dominate the earlier 4B causal adapter because count agreement regresses. It is therefore published as a research replication checkpoint, not the primary 4B deployment model.
This is a PEFT adapter and requires Qwen/Qwen3.5-4B. See the VIGIL GitHub
repository for the exact protocol and limitations.
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