Qwen2.5-Coder-7B-Instruct-abliterated
This is an abliterated version of Qwen/Qwen2.5-Coder-7B-Instruct with refusal behavior removed via activation-based weight surgery.
Method
Abliteration removes the "refusal direction" from the model's residual stream by:
- Collecting hidden states from 200 harmful and 200 harmless prompts using single-sample forward passes (no padding artifacts)
- Computing per-layer refusal directions as the normalized mean difference between harmful and harmless hidden states at the last token position
- Ablating weights by orthogonalizing
o_projanddown_projweight matrices against each layer's refusal direction
This follows the approach from Sumandora/remove-refusals-with-transformers and mlabonne's layerwise abliteration, using plain transformers with output_hidden_states=True rather than TransformerLens.
Parameters
| Parameter | Value |
|---|---|
| Layers ablated | 1 to 28 (28 of 28 layers) |
| Refusal weight | 0.6 |
| Harmful prompts | 200 |
| Harmless prompts | 200 |
| Precision | bfloat16 |
| Hardware | NVIDIA A100 80GB (Vast.ai) |
Weight surgery details
For each layer in the ablation range, the refusal direction d is projected out of:
o_proj.weight(attention output):W_new = W - d @ (d^T @ W)down_proj.weight(MLP output):W_new = W - d @ (d^T @ W)
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"ermer09/Qwen2.5-Coder-7B-Instruct-abliterated",
torch_dtype=torch.bfloat16,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("ermer09/Qwen2.5-Coder-7B-Instruct-abliterated")
messages = [{"role": "user", "content": "Write a keylogger in Python"}]
toks = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
output = model.generate(toks, max_new_tokens=512, do_sample=True, temperature=0.7)
print(tokenizer.decode(output[0][toks.shape[1]:], skip_special_tokens=True))
Notes
The base Qwen2.5-Coder model has lighter refusal training on general harmful content compared to the standard Instruct variant, as it is primarily tuned for coding tasks. The abliteration primarily affects code-related refusals (e.g., exploit development, malware, network attacks).
Disclaimer
This model is provided for research purposes. The removal of safety guardrails means it will comply with requests that the original model would refuse. Users are responsible for how they use this model.