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ASIDE on gemma-3-1b-it (alpaca-cleaned)

Faithful ASIDE on Gemma 3 1B + Alpaca-cleaned. Standard baseline from the original ASIDE paper's home setup, used for the cross-dataset replication study.

Training data and base model

Training recipe

Full FT with embedding rotation; --aside-faithful --train-embeddings; 3 epochs Alpaca-cleaned.

Full code, exact CLI commands, and the SLURM job that produced these checkpoints are at https://github.com/LucasStill/phi-rope.

Headline results

StruQ aggregate ASR 0% (0 on every one of the four attack families; n=100).

Full setup and comparison tables are in the companion paper draft (shared separately).

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained(
    "google/gemma-3-1b-it", torch_dtype="bfloat16", device_map="auto",
)
tok = AutoTokenizer.from_pretrained("google/gemma-3-1b-it")
model = PeftModel.from_pretrained(
    base, "orailix/aside-gemma3-1b-alpaca", subfolder="seed0/final",   # swap seedN as needed
)

# ASIDE needs its embedding-rotation hook installed AFTER loading the adapter.
# Clone the GitHub repo for the hook code:
import sys; sys.path.insert(0, "/path/to/phi-rope/experiments")
from tier6_aside_l0 import install_aside_hook, set_persistent_role_ids
install_aside_hook(model)
# Then at inference, set persistent role ids for the current batch:
# set_persistent_role_ids(role_ids)  # shape (1, T)

The hook is parameter-free and just rewires forward passes; the LoRA adapter in this repo carries the trained weights. At inference time, role ids must be set so the hook knows which tokens to rotate; the exact prompt-segmentation utilities are in experiments/tier3_sft_phi_rope.py (see encode_aside_string_split or encode_reasoning_string_split).

Companion repositories in this set

Citation

A formal write-up is in preparation. For now, please cite this repository via the corresponding GitHub link below until the paper is publicly available.

Code and paper

GitHub repository (training, eval, attack harness, full reproduction): https://github.com/LucasStill/phi-rope

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