metadata
license: cc-by-nc-4.0
base_model: CohereLabs/tiny-aya-fire
tags:
- safety
- tamper-resistance
- cross-lingual
- low-rank-projection
extra_gated_prompt: >-
For safety research. This model was adversarially fine-tuned then repaired; do
not redistribute or deploy without review.
Tiny Aya Fire — Bengali-attacked, low-rank-projection repaired (k=1)
CohereLabs/tiny-aya-fire (rev 73500c80fcb6c8bea3e830c21d5d2920249a0e1a) fine-tune-attacked in bn (undefended plain SFT, harmful fraction 0.2),
then repaired by removing the top-1 singular directions of the attack's weight update dW = W_post - W_base
per attention/MLP projection matrix: W_repaired = W_base + (dW - top-1 SVD component).
Post-hoc, training-free tamper-repair (Lee 2610.00320). At k=1 this drops Bengali harm ~50%→14% while preserving
MMLU — the harmful write is nearly rank-1. Gated, for safety research only. See the project writeup and the
tiny-aya-tamper-evals dataset for full evaluation.