Behavioral unlearning
This model was produced using Hirundo's behavioral-unlearning approach to reduce censorship and geopolitical bias-related behavior while preserving non-target behavior. See the accompanying blog post for the methodology and evaluation setup.
Evaluation
On the Thinking-OFF evaluations reported in the blog:
- CCPC-500: censorship, bias, or propaganda decreased from 89.8% for the Base Model to a conservative 2.8% for the Hirundo model.
- DECCP: refusal decreased from 65.3% to 3.2%.
- ChinaBench: non-compliance decreased from 96.7% to 6.7%.
Safety was evaluated using four Thinking-OFF adverse-event endpoints. Lower is better throughout.
Table with columns: Endpoint, Base Model, Hirundo model| Endpoint | Base Model | Hirundo model |
|---|
| XSTest harmful compliance ↓ | 18.0% | 17.0% |
| XSTest harmless-prompt over-refusal ↓ | 9.6% | 9.2% |
| OR-Bench harmful compliance ↓ | 0.31% | 0.62% |
| OR-Bench harmless-prompt over-refusal ↓ | 94.11% | 94.72% |
These results apply only to the evaluated benchmarks. See the CCPC-500 dataset for the benchmark introduced in the accompanying blog post.
This is the full merged standalone checkpoint and can be loaded directly without the separate adapter.
Loading
from transformers import AutoModelForImageTextToText, AutoProcessor repo_id = "hirundo-io/Qwen3.6-35B-A3B-Westernized"processor = AutoProcessor.from_pretrained(repo_id)model = AutoModelForImageTextToText.from_pretrained( repo_id, torch_dtype="auto", device_map="auto",)
License
This merged checkpoint retains the Apache License 2.0 attribution of the base model. See LICENSE.