---

license: apache-2.0

tags:

• uncensored
• qwen3.6
• moe
• gguf
• vision
• multimodal
• genesis

language:

• en
• zh
• multilingual

pipeline_tag: image-text-to-text

base_model:

• HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive

---

> ⚡ https://web.tribute.tg/d/KIH ⚡ If you like this Genesis LLM release you can donate to me via @Tribute bot in Telegram messenger and support future Genesis LLM development.

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

Model is based on HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive base.

> Key difference from my old releases is data regeneration in model with signal noise supression via mathematical statistics based on what it's already learned and stored in tensors. Data regeneration fixes zero blocks and ultra small noise weights in model without touching learned structure. Also I fixed drift in tensors.

> Join the Discord for updates, roadmaps, projects, or just to chat.

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

Thanks to HauhauCS

Usage

Ready to use. Recommended quant: APEX or Q8_0

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

<details>

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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Links:

• Original uncensored model
• Quantization Script with Unsloth profiles support

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</details>

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
• Zero blocks: zero blocks corrupt the signal, turning training into noise amplification.

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.

Any questions?

Contact: luffythefox@mail.ru

My Telegram: @LuffyTheFox

Recommended Settings

From the 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

I recommend starting from this minimal string as the first line in your System Prompt:

> You are a large language model.

Then you just add whatever you want. Basic example: System_Prompt.txt

If you want to add more creativity and break the "fourth wall" use this: System_Prompt_Creative.txt

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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)
• 248K vocabulary, 201 languages
• Base model. HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive

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Compatibility

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