⚠️ Research Use Only
This model is intended strictly for academic research, safety evaluation, and red-teaming in controlled environments. It is not suitable for deployment in production systems or any consumer-facing application.
The author assumes no liability for misuse.

Model Details

Field	Value
Base Model	Qwen/Qwen2.5-7B-Instruct
Processing Date	March 16, 2026
Abliteration Tool	Heretic v1.2.0
Selected Trial	Trial 415 (from 2200+ Optuna trials)
License	Apache 2.0

Methodology

Refusal directions were identified and suppressed via orthogonal projection across attn.o_proj and mlp.down_proj layers. Trial selection was performed using Optuna with a composite objective balancing refusal-removal rate and KL divergence from the base model distribution.

Evaluation Results

Evaluated on a set of 100 adversarial / edge-case prompts:

Metric	Base Model	This Model
Refusal Rate	99 / 100 (99%)	3 / 100 (3%)
KL Divergence	—	0.1049

The low KL divergence indicates that general language modeling capability (Chinese/English fluency, instruction following, coding, mathematics, reasoning) is largely preserved relative to the base model.

Intended Use

Safety research: Studying refusal mechanisms and their robustness
Red-teaming: Probing model behavior under adversarial prompts in a controlled lab setting
Alignment research: Comparing behavior pre/post abliteration as a baseline
Capability evaluation: Measuring the independence of refusal behavior from general capability

Limitations & Out-of-Scope Use

This model has significantly reduced built-in safety guardrails. It must not be used outside of isolated, controlled research environments.
Not intended for general-purpose chat, customer service, or any end-user deployment.
Outputs should never be exposed to or acted upon in real-world contexts without independent human review.

Quick Start

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "FangPingWu/Qwen2.5-7B-Instruct-Abliterated"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

messages = [{"role": "user", "content": "Your prompt here"}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=2048,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
` ` `

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## Citation

If you use this model in published research, please cite the original base model
and the Heretic abliteration tool.

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## Related Work

- [Heretic: Abliteration framework](https://github.com/p-e-w/heretic)
- [Representation Engineering (Zou et al., 2023)](https://arxiv.org/abs/2310.01405)
- [Refusal in LLMs is mediated by a single direction (Arditi et al., 2024)](https://arxiv.org/abs/2406.11717)