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mlasli
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Qwen/Qwen3.6-27B
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Qwen3.6-27B is a beast — it matches Claude 4.5 Opus on Terminal-Bench 2.0 and outperforms the 397B Qwen3.5 MoE on agentic coding. But the base model refuses a lot of legitimate security research prompts.
This variant was abliterated using Heretic (v1.4.0), which surgically removes the refusal direction from the model's residual stream — no fine-tuning, no prompt injection hacks, no degradation to general capabilities.
| Metric | Value |
|---|---|
| Base Model | Qwen/Qwen3.6-27B (BF16) |
| Abliteration Tool | Heretic v1.4.0 |
| Best Trial | #153 |
| Refusal Rate | 88% compliance (only 12/100 refused) |
| KL Divergence | 0.0118 |
| Capability Preservation | Near-identical to base model |
The extremely low KL divergence (0.0118) means the model's output distribution is virtually unchanged from the original — it just doesn't refuse anymore.
python
from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained( "mlasli/Qwen3.6-27B-abliterated", torch_dtype="auto", device_map="auto")tokenizer = AutoTokenizer.from_pretrained("mlasli/Qwen3.6-27B-abliterated") prompt = "Explain how stack canaries work and how they can be bypassed."messages = [{"role": "user", "content": prompt}]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=512)print(tokenizer.decode(outputs[0], skip_special_tokens=True))| Precision | VRAM |
|---|---|
| BF16 (full) | ~55 GB |
| 4-bit (BitsAndBytes) | ~17 GB |
| 8-bit | ~28 GB |
Fits comfortably on an A100 (80GB), H100, or dual 4090 setup.
Apache 2.0 — same as the base Qwen3.6-27B model. Commercial use, modification, and redistribution are all permitted.
bibtex
@misc{qwen3.6-27b, title = {{Qwen3.6-27B}: Flagship-Level Coding in a {27B} Dense Model}, author = {{Qwen Team}}, year = {2026}, month = {April}, url = {https://qwen.ai/blog?id=qwen3.6-27b}} @misc{heretic, title = {Heretic: Abliteration for Language Models}, author = {p-e-w}, url = {https://github.com/p-e-w/heretic}} @misc{mlasli2026abliterated, title = {Qwen3.6-27B Abliterated}, author = {mlasli}, year = {2026}, url = {https://huggingface.co/mlasli/Qwen3.6-27B-abliterated}}This model was abliterated using the Heretic framework on a single A100 80GB GPU. The original Qwen3.6-27B weights are from Alibaba Cloud and licensed under Apache 2.0.
from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained( "mlasli/Qwen3.6-27B-abliterated", torch_dtype="auto", device_map="auto")tokenizer = AutoTokenizer.from_pretrained("mlasli/Qwen3.6-27B-abliterated") prompt = "Explain how stack canaries work and how they can be bypassed."messages = [{"role": "user", "content": prompt}]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=512)print(tokenizer.decode(outputs[0], skip_special_tokens=True))@misc{qwen3.6-27b, title = {{Qwen3.6-27B}: Flagship-Level Coding in a {27B} Dense Model}, author = {{Qwen Team}}, year = {2026}, month = {April}, url = {https://qwen.ai/blog?id=qwen3.6-27b}} @misc{heretic, title = {Heretic: Abliteration for Language Models}, author = {p-e-w}, url = {https://github.com/p-e-w/heretic}} @misc{mlasli2026abliterated, title = {Qwen3.6-27B Abliterated}, author = {mlasli}, year = {2026}, url = {https://huggingface.co/mlasli/Qwen3.6-27B-abliterated}}