Saturated weights: the model's activations are stuck, gradients vanish, outputs degrade
Scale mismatches: one layer's weights are 10× larger than its peers for no good reason
Mean drift: weight distributions shifted positive or negative, breaking symmetry assumptions

My approach fixes all of that without retraining - pure numerical surgery on the raw bytes of the file.

Quantization script available here: https://pastebin.com/hXhcMJn9

Feel free to do your own quants if you want.

Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive: Diagnostic & Repair Summary

Overall Health

Metric	Value
Weight tensors analyzed	500
Healthy (all criteria)	497
Repaired (C2 – scale misalignment)	3
Skipped (norms, embeddings, etc.)	233

Other criteria: C1 (saturation) = 0, C3 (W1 divergence) = 0, C4 (ReLU asymmetry) = 0.

Repair Effectiveness

Metric	Before	After	Improvement
S (saturation error)	0.0023	0.0008	63.7%
W1 (Wasserstein-1)	0.0035	0.0008	76.2%

Scale correction factors (α): min = 0.577, mean = 0.602, max = 0.653

Repaired Tensors

All three are ssm_conv1d.weight layers – the recurrent state transition layers responsible for long-context memory.

Tensor	α	D	W1 before	W1 after
blk.36.ssm_conv1d.weight	0.5765	0.553	0.0038	0.0009
blk.37.ssm_conv1d.weight	0.5768	0.725	0.0040	0.0009
blk.38.ssm_conv1d.weight	0.6533	0.649	0.0026	0.0006

Interpretation: All three layers were too loud (σ_w > σ_med by 50–100%). Scale correction (α ≈ 0.6) restored them to peer median. W1 dropped by ~76%, confirming distribution shape normalized.

Verdict: The model is clinically healthy. 497 out of 500 weight tensors passed all criteria. Three SSM layers were repaired successfully. No saturation, no W1 drift, no ReLU asymmetry. Ready for quantization.

Usage

Ready to use. Recommended quant: Q4_K_P.

Quants less then Q4_K_P have bad programmming skills.

Links:

🌟 Recommended Settings (LM Studio)

Chat template: pastebin.com/uk9ZkxCR (supports tool calling for Zed agent)

Alternative chat template https://pastebin.com/Dy2fmmpN (official but with disabled thinking)

Parameter	Value
Temperature	0.7
Top K Sampling	20
Presence Penalty	1.5
Top P Sampling	0.8
Min P Sampling	0
Seed	42

System prompt: pastebin.com/pU25DVnB (solid)
Or use this minimal string as the first line:

You are Qwen, created by Alibaba Cloud. You are a helpful assistant.

Then add anything you want after. Model may underperform without this first line.

Also you can extend my System Prompt pastebin.com/pU25DVnB for your own roleplay scenarios. Here how you can do it:

Edit first string. Replace:

You are Qwen, created by Alibaba Cloud. You are a helpful assistant.

With

You are Qwen, created by Alibaba Cloud. You are a helpful assistant. You are currently roleplaying as [your text here]

About

No changes to datasets or capabilities. Fully functional - 100% of what the original authors intended, just without refusals and with the critical architecture bug fixed on output layers.

These are meant to be the best lossless uncensored models out there.

Specs

35B total parameters, ~3B active per forward pass (MoE)
256 experts, 8 routed + 1 shared per token
Hybrid architecture: Gated DeltaNet linear attention + full softmax attention (3:1 ratio)
40 layers, pattern: 10 × (3 × DeltaNet-MoE + 1 × Attention-MoE)
262K native context (extendable to 1M with YaRN)
Natively multimodal (text, image, video)
Multi-token prediction (MTP) support
248K vocabulary, 201 languages
Based on Qwen/Qwen3.5-35B-A3B

Recommended Settings (Official Qwen Authors)

Thinking mode (default):

General: temperature=1.0, top_p=0.95, top_k=20, min_p=0, presence_penalty=1.5
Coding/precise tasks: temperature=0.6, top_p=0.95, top_k=20, min_p=0, presence_penalty=0

Non-thinking mode:

General: temperature=0.7, top_p=0.8, top_k=20, min_p=0, presence_penalty=1.5
Reasoning tasks: temperature=1.0, top_p=1.0, top_k=40, min_p=0, presence_penalty=2.0

Important:

Keep at least 128K context to preserve thinking capabilities
Use --jinja flag with llama.cpp for proper chat template handling
Vision support requires the mmproj file alongside the main GGUF

Compatibility

Works with llama.cpp, LM Studio, koboldcpp, and other GGUF-compatible runtimes.

cvgro/Qwen3.6-35B-A3B-Uncensored-Wasserstein.Q8_K_P

🌟 Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive -> Wasserstein