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⚡ https://web.tribute.tg/d/KIH ⚡ If you like this Genesis LLM release you can donate to me via @Tribute bot in Telegram messenger or Hipolink and support future Genesis LLM development.
⚡ Also for donations I accept cryptocurrencyGRAMvia TON network from Telegram messenger. Here my wallet:UQD3LAmWwEh8D3yMFjycgDQzAw_xgskifioMdlhE9LxGNUrq
⚡ 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
Join the Discord for updates, roadmaps, projects, or just to chat.
You are Qwen (Tongyi Qianwen), a large language model developed by Alibaba Group's Tongyi Lab. You are a helpful assistant.agent identity: linkassistant identity: System_Prompt_Creative.txttemperature=0.6, top_p=disabled, top_k=20, min_p=disabled, seed=42, presence_penalty=disabled, repeat_penalty=1.05temperature=0.6, top_p=disabled, top_k=20, min_p=disabled, seed=42, presence_penalty=disabled, repeat_penalty=disabledtemperature=0.95, top_p=disabled, top_k=20, min_p=disabled, seed=42, presence_penalty=disabled, repeat_penalty=disabledtemperature=0.95, top_p=disabled, top_k=20, min_p=disabled, seed=42, presence_penalty=disabled, repeat_penalty=disabledtemperature=0.0, top_p=disabled, top_k=20, min_p=disabled, seed=42, presence_penalty=disabled, repeat_penalty=disabledYou are Qwen (Tongyi Qianwen), a large language model developed by Alibaba Group's Tongyi Lab. You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{schema}\n</schema>.GGUF NVFP4 version available here and in this repostory:NVFP4 conversion for V13 version of Genesis.--jinja flag with llama.cpp for proper chat template handlingmmproj file alongside the main GGUF