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Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V6-GGUF
⚡ 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.
⚡ Why Genesis project exists? During training, ALL models don't just learn knowledge - they also accumulate random noise in their tensors. This noise builds up and creates something I call the Noise Gate - a fundamental barrier that stops LLM models from learning further and makes them unstable, verbose, and prone to hallucinations. My approach reduces this noise. It repairs the signal without touching the learned knowledge and gradient. The result is a model that finally speaks clearly, follows instructions, and remembers context - because it's no longer fighting its own internal chaos.
⚡ What is Genesis? Genesis is post training data regeneration and calibrarion algorythm for neural networks (LLM) in GGUF format that I made with AI help during almost half a year of development. It's optimized, architecture independent, works with any model and based on mathematical statistics. I don't train or finetune models, I repair purity of signal in them instead on Google Collab Free on Tesla T4 GPU via Python based on how models learns information. On first stage I scan ssm_conv1d tensors in model, they handle long context memory. I repair balance in ssm_conv1d tensors via custom SVD. On second stage I scan model and detect noise in tensors via custom SVD. During scanning I exclude token_embd.weight, output.weight, ffn_gate_inp.weight, ffn_gate_inp_shexp.weight, 1D tensors, bias and norms. Then I reduce training noise in tensors via custom SVD with preserved training data, 99% of siginal and learned gradient. On third stage, 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 in model
Model is based on HauhauCS/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive base.
Thanks to HauhauCS
Join the Discord for updates, roadmaps, projects, or just to chat.
This model is useful for uncensored local coding with LM Studio - Bionic agent.
Ready to use. Recommended quant: Q3_K_M (16GB) or Q4_K_M (24 GB)
Recommended GPU VRAM: not less than 16 GB of VRAM, 24 GB VRAM is recommended
Tensor drift repair by me. Method: Genesis
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 F16.
Set GPU offload to maximum.
For best model stability and first experience I recommend starting from this string in your System Prompt with enabled thinking and nothing else:
You are Qwen, a large language model created by Tongyi Lab team from Alibaba Group. You are a helpful assistant.
or this string
You are a helpful assistant.
If you want to bring more creativity to model use this System Prompt: System_Prompt_Creative.txt
Thinking mode (coding):
temperature=0.6, top_p=0.95, top_k=20, min_p=0, seed=42, presence_penalty=disabled, repeat_penalty=disabledtemperature=1.0, top_p=0.95, top_k=20, min_p=0.05, seed=42, presence_penalty=disabled, repeat_penalty=disabledNon Thinking mode (creative):
temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=42, presence_penalty=disabled, repeat_penalty=disabledImportant:
--jinja flag with llama.cpp for proper chat template handlingmmproj file alongside the main GGUF16 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN))Works with llama.cpp, LM Studio, koboldcpp, and other GGUF-compatible runtimes.