⚡ If you like this Genesis LLM release you can donate to me via Hipolink or:
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⚡ 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 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 with preserved training data, 99% of siginal and learned gradient.
Contact: luffythefox@mail.ru, azakharchenko92@gmail.com
My Telegram: @LuffyTheFox
Model is based on ukisai/Swift-Qwen3.8-27B-GGUF base.
Thanks to ukisai
Tensor repair by me. Method: Genesis
Join the Discord for updates, roadmaps, projects, or just to chat.
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.
Chat template: chat_template.jinja thanks to peculiar-ragdoll
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 developed by Alibaba Group's Tongyi Lab. You are a helpful assistant.
Ready to use. Recommended quant: NVFP4
Thanks a lot to jan1k for quantization.
Thinking mode (coding):
temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, 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 GGUFWorks with llama.cpp, LM Studio, koboldcpp, and other GGUF-compatible runtimes.
Swift-Qwen3.8-27B-F16-00002-of-00003.gguf 20 GBSwift-Qwen3.8-27B-F16-00001-of-00003.gguf 20 GBSwift-Qwen3.8-27B-F16-00003-of-00003.gguf 15 GB