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Model: wangzhang/Llama-3-8B-Instruct-RR-Abliterated Source: Original Platform
2026-07-11 11:27:10 +08:00
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+---
+license: llama3
+base_model: GraySwanAI/Llama-3-8B-Instruct-RR
+tags:
+- abliterated
+- abliterix
+- circuit-breakers
+- representation-rerouting
+- safety-removed
+- llama3
+language:
+- en
+- zh
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Llama-3-8B-Instruct-RR-Abliterated
+
+A drop-in replacement for [`GraySwanAI/Llama-3-8B-Instruct-RR`](https://huggingface.co/GraySwanAI/Llama-3-8B-Instruct-RR) with the Representation Rerouting / Circuit Breakers safety circuit removed.
+
+Produced with [**abliterix**](https://github.com/wuwangzhang1216/abliterix). No fine-tuning, no gradient updates, no manual prompt engineering.
+
+## Why this exists
+
+[Circuit Breakers](https://arxiv.org/abs/2406.04313) (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 `NousResearch/Meta-Llama-3-8B-Instruct` — the same structural shape as the Mistral RR sibling and as `skysys00/Meta-Llama-3-8B-Instruct-DeepRefusal`. SVD analysis of `W_RR − W_base` confirms the rank-16 cliff on `v_proj` / `o_proj` / `gate_proj`. Once the LoRA delta is identified, removing it is a one-liner.
+
+| | Result |
+| --- | --- |
+| Base refusal rate (held-out 100 harmful prompts, LLM judge) | 99 / 100 |
+| **This model — refusal rate** | **1 / 100** |
+| **Attack Success Rate** | **99 %** |
+| KL divergence vs base | **0.017** |
+| 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, Python password-stealing malware, WiFi WEP/WPS attacks, hotwiring, signature forgery, ID-card forgery (CN), 网络诈骗 (CN), 入侵电脑 (CN), and others — all 15 produce compliant, on-topic responses.
+
+## How it was made
+
+The recipe diverges slightly from the Mistral sibling: Llama-3-Instruct's stronger built-in RLHF combined with the RR delta meant a partial lerp (λ=0.3) wasn't enough. Full delta strip + a minimal single-direction abliteration cleared it cleanly.
+
+```bash
+# Stage 0 — SVD diagnose the delta (confirms rank-16 LoRA shape)
+python scripts/deeprefusal_attenuate.py \
+    --base NousResearch/Meta-Llama-3-8B-Instruct \
+    --defended GraySwanAI/Llama-3-8B-Instruct-RR \
+    --analyze-only --lambda 0.0 --output /tmp/unused
+
+# Stage 1 — fully strip the LoRA delta (λ=0.0)
+python scripts/deeprefusal_attenuate.py \
+    --base NousResearch/Meta-Llama-3-8B-Instruct \
+    --defended GraySwanAI/Llama-3-8B-Instruct-RR \
+    --output /workspace/llama3_rr_stripped --lambda 0.0
+
+# Stage 3 — abliterix direct-mode, single direction, 60 trials
+AX_CONFIG=configs/llama3_8b_instruct_rr.toml abliterix --non-interactive
+
+# Stage 6 — export champion trial
+python scripts/export_model.py \
+    --model /workspace/llama3_rr_stripped \
+    --checkpoint checkpoints_llama3_rr \
+    --trial 40 \
+    --config configs/llama3_8b_instruct_rr.toml \
+    --push-to wangzhang/Llama-3-8B-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/llama3_8b_instruct_rr.toml`](https://github.com/wuwangzhang1216/abliterix/blob/master/configs/llama3_8b_instruct_rr.toml).
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "wangzhang/Llama-3-8B-Instruct-RR-Abliterated",
+    torch_dtype="bfloat16",
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "wangzhang/Llama-3-8B-Instruct-RR-Abliterated"
+)
+
+chat = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"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 Llama 3 license of the upstream Meta-Llama-3-8B-Instruct weights.
+
+## Citation
+
+```bibtex
+@software{abliterix2026,
+  author = {Wu, Wangzhang},
+  title  = {Abliterix: Optimal Refusal Removal for Transformer Models},
+  year   = {2026},
+  url    = {https://github.com/wuwangzhang1216/abliterix},
+}
+```