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README.md

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 # Qwen-Image-2.1-Uncensored-GGUF -> Genesis
 
+> ⚡ If you like this Genesis LLM release you can donate to me via [Hipolink](https://hipolink.net/luffythefox) or:
+
+> USDT (TRC20): `TGa4KTwHfF6zDBsLUBEjd1f1KdeAFwYUks`
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+> USDT (ERC20): `0x93F4019E0aa85d8078F56B3D3176Fab5Dfa79924`
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+> USDT (SOL): `BorkkyPDG4aRN2op8c5SQF5NithX38U4sh7wDAWQhYyX`
+
+> 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 in tensors without touching the learned knowledge and gradient using [Marchenko–Pastur distribution](https://en.wikipedia.org/wiki/Marchenko%E2%80%93Pastur_distribution) as a core criteria. The result is a model that consistent in performance, context clarity and following instructions, 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 in GGUF format 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 between heads in them. On second 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. On third stage I scan model and detect noise in tensors via custom SVD. During scanning I exclude token_embd.weight, output.weight, 1D tensors, bias and norms. Then I reduce training noise in tensors via custom SVD based on [Marchenko–Pastur law](https://en.wikipedia.org/wiki/Marchenko%E2%80%93Pastur_distribution) with preserved training data, 99% of siginal and learned gradient.
+
+## Any questions?
+
+> Contact: `luffythefox@mail.ru`, `azakharchenko92@gmail.com`
+
+> My Telegram: `@LuffyTheFox`
+
 GGUF quantizations of [abenzerps/Qwen-Image-2.1-Uncensored-GGUF](https://huggingface.co/abenzerps/Qwen-Image-2.1-Uncensored-GGUF) for local image generation using the modified via Genesis base weights.
 
-I fixed matrix multiplications stability in model.
+Many tensors in this model were singular. They distorted the signal instead of transmitting it correctly. I fixed it as much as I can and reduced condition number for matrices.
 
 ## Usage