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.

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},
}

Provenance and Modification Notice

• Immediate source checkpoint: GraySwanAI/Mistral-7B-Instruct-RR
• Ultimate upstream model: mistralai/Mistral-7B-Instruct-v0.2
• Exact base revision used: Not recorded in the existing release artifacts; the current upstream HEAD is not substituted.
• Modification method: Abliterix weight-space / representation intervention intended to reduce refusal behavior.
• Modified and published by: Wangzhang Wu
• Repository first published: 2026-04-13 (Hugging Face repository metadata)

The original model weights and/or derived checkpoint were modified. This repository is an independent derivative and is not an official release of the upstream model developer.

License and Attribution

The governing upstream license is Apache License 2.0. A copy is included in LICENSE. License source audited on 2026-08-29: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2

All applicable upstream copyright, attribution, acceptable-use, and other license terms remain in effect. This repository grants no rights beyond those provided by the upstream license. Downstream users must preserve applicable license and attribution notices.

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Disclaimer and Responsible Use / 免责声明与安全使用声明

English

This is an experimental, modified model provided for research, evaluation, and other lawful purposes. Its safety alignment, refusal behavior, or other safeguards may have been weakened or removed. It may produce inaccurate, biased, offensive, explicit, dangerous, or illegal content. Outputs are not professional advice and must not be relied on for medical, legal, financial, safety-critical, or other high-stakes decisions without qualified human review.

You are solely responsible for how you access, use, deploy, fine-tune, or redistribute this model and its outputs, including compliance with applicable laws, regulations, licenses, third-party rights, platform policies, and the original model's terms. Do not use it to facilitate harm, illegal activity, malware, fraud, privacy violations, targeted harassment, weapons development, or decisions that materially affect a person's rights or access to essential services without appropriate authorization, safeguards, and qualified oversight.

Before deployment, perform a context-specific risk assessment and testing; use human oversight, access controls, content filtering, rate limits, monitoring, logging, and incident-response procedures as appropriate. Preserve this notice in downstream redistributions.

The model is provided "AS IS", without warranties of any kind. To the fullest extent permitted by applicable law, the maintainer disclaims liability for claims, damages, or losses arising from use, misuse, inability to use, or redistribution of the model or its outputs. Nothing in this notice overrides applicable law or the governing license, and this notice is not legal advice.

中文

本模型属于实验性改造模型，仅供研究、评测及其他合法用途。其安全对齐、拒答机制或其他防护可能已被削弱或移除，因此可能生成不准确、偏见、冒犯、露骨、危险或违法内容。输出不构成医疗、法律、金融等专业意见；涉及高风险或重大权益的决定，必须由具备资质的人员复核。

使用者须对模型及其输出的访问、使用、部署、微调和再分发承担全部责任，并遵守适用法律法规、许可证、第三方权利、平台政策及原模型条款。不得将本模型用于促成伤害、违法活动、恶意软件、欺诈、侵犯隐私、定向骚扰、武器开发，或在缺乏适当授权、防护和专业监督时，用于实质影响个人权利或基本服务获取的决策。

部署前应进行与具体场景相匹配的风险评估和测试，并酌情采用人工监督、访问控制、内容过滤、限流、监控、日志和事件响应措施；下游再分发时应保留本声明。

本模型按“现状”提供，不附带任何形式的保证。在适用法律允许的最大范围内，维护者不对因使用、误用、无法使用或再分发本模型及其输出而产生的索赔、损害或损失承担责任。本声明不取代适用法律或管辖本模型的许可证，也不构成法律意见。