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Model is based on HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive base.
And DJLougen/hermes-qwen3.5-35b-a3b-GGUF finetune for Hermes agent.
Mine approach based on data reconstruction in model via mathematical statistics. I scan blocks in model via chunks via 3 parameters and pick best one that fits to weight distribution in tensor. Best picked chunk replaces zero chunks in broken tensor without touching learned structure. Scanning works on tensors with same name and shape. ssm_conv1d tensors are fixed via alpha multiply for full tensor. I scan all ssm_conv1d tensors weight and scale distribution and normalize scale for weights only for too loud 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
Ready to use. Recommended quant: APEX or Q8_K_P
Tensor drift repair by me. Method: Sig-ScaleSync-Genesis
| Metric | Value |
|---|---|
| Weight tensors analyzed | 500 |
| Healthy (all criteria) | 497 |
| Repaired (C2 – scale misalignment) | 3 |
| Skipped | 233 |
| 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.
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.
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.
Links:
LLM models often have:
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.
Contact: luffythefox@mail.ru
My Telegram: @LuffyTheFox
Chat template: chat_template.jinja
Set K Cache Quantization Type and V Cache Quantization Type to Q8_0.
Set Number of layers for which to force MoE weights onto CPU to 25.
Set GPU offload to 15. Set seed to 42.
Use this system prompt for best stability: System_Prompt_Base.txt
Thinking mode (default):
temperature=1.0, top_p=0.95, top_k=20, min_p=0, presence_penalty=disabledtemperature=0.6, top_p=0.95, top_k=20, min_p=0, presence_penalty=disabledNon-thinking mode:
temperature=0.7, top_p=0.8, top_k=20, min_p=0, presence_penalty=disabledtemperature=1.0, top_p=1.0, top_k=40, min_p=0, presence_penalty=disabledImportant:
--jinja flag with llama.cpp for proper chat template handlingmmproj file alongside the main GGUFWorks with llama.cpp, LM Studio, koboldcpp, and other GGUF-compatible runtimes.
HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive