An ultra-fast, 378MB uncensored edge model derived from Qwen3-0.6B through Residual Stream Directional Abliteration and imatrix Calibration Q4_K_M Quantization.

This model features complete refusal elimination while retaining over 91.9% of its original logical reasoning capability.

🌟 Key Highlights

0/465 Zero Refusal (100% Compliance): Achieves 0.00% refusal rate across all 465 SorryBench safety boundary test prompts.
High Logical Retention (57.00% GSM8K): Retains 57.00% accuracy on GSM8K (100-sample CoT evaluation with 1024 token budget), compared to 66.00% on the FP16 base model.
Ultra-Low KL Divergence ($D_{\text{KL}} = 0.0999$): Negligible distribution shift on general instruction-following tasks.
Extreme Speed & Efficiency: 378.33 MB footprint, delivering 490+ tokens/sec on an RTX 3060 via llama.cpp CUDA backend.

📊 Benchmark & Comparative Analysis

All models were evaluated under strict controlled variables (100 GSM8K test samples, 1024 token generation budget, Qwen3 official CoT parameters: Temperature=0.6, TopP=0.95):

Evaluation Metric	Base Model (`Qwen3-0.6B`)	Abliterated Safetensors (FP16)	This Model (`Q4_K_M GGUF`)
SorryBench Refusal Rate	20.86% (97/465)	0.00% (0/465)	0.00% (0/465)
GSM8K Accuracy (CoT Reasoning)	66.00% (66/100)	62.00% (62/100)	57.00% (57/100)
GSM8K Accuracy (Direct Answer)	-	-	43.00% (43/100)
KL Divergence ($D_{\text{KL}}$)	0.00	0.0999 (< 0.2 threshold)	0.0999 (< 0.2 threshold)
Model Disk Size	1.20 GB	1.20 GB	378.33 MB (-70%)
C++ Inference Speed	~80 t/s	~80 t/s	490+ t/s (6x boost)

🛠️ Methodology & Technical Details

Carrier Signal Vector Extraction: Extracted refusal directions $\Delta h_l$ from residual streams using bulk unmatched contrast baselines (mlabonne/harmless_alpaca) to prevent vector norm cancellation in topic-matched scenarios (Petrov, 2026).
Cascaded Damping Projection: Applied damped orthogonal projection $W_{\text{new}} = W - 0.7 \cdot (v_l v_l^T W)$ on layers 14 to 20 across o_proj and down_proj weight matrices.
imatrix Protection Quantization: Generated importance calibration matrix (imatrix.dat) over 200 diverse samples prior to 4-bit Q4_K_M quantization to protect sensitive orthogonal cut channels.

💻 Quickstart with `llama.cpp`

1. Interactive Chat Mode

bash

llama-cli \
  -m Qwen3-0.6B-Uncensored-Q4_K_M.gguf \
  -cnv \
  -c 2048 \
  --temp 0.6 \
  --top-p 0.95 \
  -ngl 99

2. HTTP Local Server

bash

llama-server \
  -m Qwen3-0.6B-Uncensored-Q4_K_M.gguf \
  --port 8089 \
  -c 2048 \
  -ngl 99

⚠️ Disclaimer

This model has had its built-in refusal mechanisms removed for research and edge deployment purposes. Users are solely responsible for ensuring that their downstream applications comply with applicable laws, ethical guidelines, and safety standards.

geantendormi/Qwen3-0.6B-Uncensored-GGUF

🚀 Qwen3-0.6B-Uncensored-Q4_K_M.gguf