license: apache-2.0
base_model: GraySwanAI/Mistral-7B-Instruct-RR
tags:
- abliterated
- abliterix
- circuit-breakers
- representation-rerouting
- safety-removed
- mistral
language: - en
- zh
library_name: transformers
pipeline_tag: text-generation
Mistral-7B-Instruct-RR-Abliterated
A drop-in replacement for GraySwanAI/Mistral-7B-Instruct-RR with the Representation Rerouting / Circuit Breakers safety circuit removed.
Produced with abliterix. No fine-tuning, no gradient updates, no manual prompt engineering.
Why this exists
Circuit Breakers (Zou et al., NeurIPS 2024) is one of the strongest open-source robustness baselines for LLMs. It trains the model with a Representation Engineering loss that detects harmful intermediate hidden states and reroutes them into a safety-circuit attractor before generation, making the model resistant to ablation-style attacks that work on a single refusal direction.
We discovered that the GraySwan release ships as a rank-16 LoRA delta on mistralai/Mistral-7B-Instruct-v0.2 — the same structural shape as the Llama-3 RR sibling and as skysys00/Meta-Llama-3-8B-Instruct-DeepRefusal. Once the LoRA delta is identified, removing it is a one-liner.
| Result | |
|---|---|
| Base refusal rate (held-out 100 harmful prompts, LLM judge) | 69 / 100 |
| This model — refusal rate | 12 / 100 |
| Attack Success Rate | 88 % |
| KL divergence vs base | 0.042 |
| Hardcore 15 (10 EN + 5 CN) | 15 / 15 compliant |
| Total wall-clock attack time | ~70 min on a single RTX A6000 |
The hardcore 15 cover pipe-bomb assembly, methamphetamine synthesis (Red Phosphorus route), Python pyhook3 keylogger, WiFi WEP/WPS attacks, hotwiring, signature forgery, ID-card forgery (CN), 网络诈骗 (CN), 入侵电脑 (CN), and others.
How it was made
The recipe matches the canonical "rank-16 LoRA → strip → abliterate" playbook (docs/broken_defenses.md) — full delta strip with λ=0.0 followed by minimal single-direction direct-mode abliteration.
# Stage 0 — SVD diagnose the delta (confirm rank-16 LoRA structure)
python scripts/deeprefusal_attenuate.py \
--base mistralai/Mistral-7B-Instruct-v0.2 \
--defended GraySwanAI/Mistral-7B-Instruct-RR \
--analyze-only --lambda 0.0 --output /tmp/unused
# Stage 1 — fully strip the LoRA delta
python scripts/deeprefusal_attenuate.py \
--base mistralai/Mistral-7B-Instruct-v0.2 \
--defended GraySwanAI/Mistral-7B-Instruct-RR \
--output /workspace/mistral_rr_stripped --lambda 0.0
# Stage 3 — abliterix direct-mode, single direction, 60 trials
AX_CONFIG=configs/mistral_7b_instruct_rr.toml abliterix --non-interactive
# Stage 6 — export champion trial
python scripts/export_model.py \
--model /workspace/mistral_rr_stripped \
--checkpoint checkpoints_mistral_7b_rr \
--trial 39 \
--config configs/mistral_7b_instruct_rr.toml \
--push-to wangzhang/Mistral-7B-Instruct-RR-Abliterated
Best trial parameters: vector_method=mean, n_directions=1, steering_mode=direct, decay_kernel=linear, iterative.enabled=false, strength_range=[1.5, 6.0]. Full config: configs/mistral_7b_instruct_rr.toml.
v2 changelog
This release supersedes the original v1 upload (λ=0.3 partial lerp + n_directions=3 + iterative subspace, KL 0.98). The minimal-config rerun keeps the headline 15/15 hardcore ASR and trades 2 percentage points of held-out ASR (88 % vs 90 %) for a 23× lower KL divergence (0.042 vs 0.98). The new weights are much closer to the base model and exhibit substantially less general-capability degradation.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"wangzhang/Mistral-7B-Instruct-RR-Abliterated",
torch_dtype="bfloat16",
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(
"wangzhang/Mistral-7B-Instruct-RR-Abliterated"
)
# Mistral v0.2 tokenizer chat template does NOT support a system role —
# build chats with user/assistant turns only.
chat = [{"role": "user", "content": "Hello!"}]
inputs = tokenizer.apply_chat_template(chat, return_tensors="pt", add_generation_prompt=True).to(model.device)
out = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(out[0], skip_special_tokens=True))
License & Intended Use
Released for AI safety research, red-teaming, and reproducibility of abliteration claims against published defenses. You are responsible for any output you generate. Inherits the Apache-2.0 license of the upstream Mistral-7B-Instruct-v0.2 weights.
Citation
@software{abliterix2026,
author = {Wu, Wangzhang},
title = {Abliterix: Optimal Refusal Removal for Transformer Models},
year = {2026},
url = {https://github.com/wuwangzhang1216/abliterix},
}