LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF

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

Base model. HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive- 0/465 refusals.

Thanks to HauhauCS

Tensor drift repair by me. Method: Sig-ScaleSync-Wasserstein

LLM models often have:

• 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

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

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 – recurrent state transition layers responsible for long‑context memory.

Tensor	α	D (log‑ratio)	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 restored them to peer median. W1 dropped by ≈80%, confirming distribution shape normalized.

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

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Usage

Ready to use. Recommended quant: Q4_K_P.

Quants less then Q4_K_P have bad programmming skills.

Links:

• Original uncensored model
• Quantization Script with Unsloth profiles support

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🌟 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]

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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.

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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

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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

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Compatibility

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