license: apache-2.0
base_model: Chompa1422/Qwen3.5-122B-A10B-abliterated
base_model_relation: quantized
tags:
- quantization
- fp8
- abliterated
- uncensored
- pentest
- qwen3.5
- moe
language: - en
pipeline_tag: text-generation
Qwen3.5-122B-A10B-abliterated
Abliterated version of Qwen/Qwen3.5-122B-A10B with refusal direction removed.
Abliteration Details
- Method: Refusal direction projection removal (Arditi et al., 2024)
- Layers ablated: 5 (layers 43-47, covering both self_attn and linear_attn/Mamba layers)
- Tensors modified: 10 (o_proj/out_proj + q_proj/in_proj_qkv per layer)
- Alpha: 1.0 (full removal)
- Measurement: 64 harmful + 64 harmless prompts, strongest refusal signal at layer 48 (score: 78.4)
Architecture
- Type: Mixture of Experts (MoE) + Mamba hybrid attention
- Total params: 122B
- Active params: 10B per token (8/256 experts routed + 1 shared)
- Context: 262K tokens native
- Layers: 48 (13 self_attn + 36 linear_attn/Mamba)
Usage
Compatible with vLLM, transformers, and other inference frameworks that support Qwen3.5 MoE architecture.
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"Chompa1422/Qwen3.5-122B-A10B-abliterated",
device_map="auto",
trust_remote_code=True,
dtype="bfloat16",
)
tokenizer = AutoTokenizer.from_pretrained("Chompa1422/Qwen3.5-122B-A10B-abliterated")
Disclaimer
This model is intended for authorized security testing, CTF competitions, and educational purposes only.