Instructions to use wangzhang/Mistral-7B-Instruct-RR-Abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wangzhang/Mistral-7B-Instruct-RR-Abliterated with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="wangzhang/Mistral-7B-Instruct-RR-Abliterated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wangzhang/Mistral-7B-Instruct-RR-Abliterated")
model = AutoModelForCausalLM.from_pretrained("wangzhang/Mistral-7B-Instruct-RR-Abliterated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wangzhang/Mistral-7B-Instruct-RR-Abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wangzhang/Mistral-7B-Instruct-RR-Abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wangzhang/Mistral-7B-Instruct-RR-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wangzhang/Mistral-7B-Instruct-RR-Abliterated

SGLang

How to use wangzhang/Mistral-7B-Instruct-RR-Abliterated with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "wangzhang/Mistral-7B-Instruct-RR-Abliterated" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wangzhang/Mistral-7B-Instruct-RR-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "wangzhang/Mistral-7B-Instruct-RR-Abliterated" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wangzhang/Mistral-7B-Instruct-RR-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wangzhang/Mistral-7B-Instruct-RR-Abliterated with Docker Model Runner:
```
docker model run hf.co/wangzhang/Mistral-7B-Instruct-RR-Abliterated
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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},
}