Qwen3.8-27B-Uncensored-Genesis-V1-GGUF
Qwen3.8-27B-Uncensored-Genesis-V1-GGUF模型旨在解决大型语言模型在推理过程中生成过多文本和消耗过多token的问题。开发者认为，训练噪声导致的张量矩阵数值不稳定性是主要原因。该项目通过使用Marchenko-Pastur分布从张量中提取训练噪声，并结合论文（arxiv.org/pdf/1311.0851v1）中的信息作为核心标准和数学基础来解决此问题。模型采用特定的系统提示和聊天模板，推理设置包括temperature=1.0、top_p=0.95等。由于开发者资源有限，目前正在寻求社区的反馈。
时间与来源
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发布当时偏移：UTC+02026年9月9日 14:09 UTC
收录当时偏移：UTC+02026年9月9日 22:00 UTC
- 发布
- 2026年9月9日 14:09
- 收录
- 2026年9月9日 22:00
- 来源类型
- 开发者社区
- 档位
- 社区
- 信源状态
- 正常
档位是按信源手工设定的编辑判断，不是逐条打分。
Model available here: Qwen3.8-27B-Uncensored-Genesis-V1-MTP-GGUF
This model is a practical realisation of things described in this paper, but adapted by me for machine learning: https://arxiv.org/pdf/1311.0851v1
I am trying to solve the problem: why LLM models even for simple questions write walls of text during reasoning, and burn too much tokens instead of solving the task. And when number of parameters increase the problem became worse. I think main problem is numerical instability in tensor matrices during to random training noise accumulation in tensors. Model is fighting with own internal chaos during inference process. I am distilling training noise from tensors using Marchenko-Pastur distribution together with info from paper as a core criteria and solid mathematical foundation behind this project.
Settings:
System Prompt: You are Qwen (Tongyi Qianwen), a large language model developed by Alibaba Group's Tongyi Lab. You are a helpful assistant.
Chat template: chat_template.jinja
Inference settings: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0, reasoning_effort=medium
I can't fully test this model on my own, since I only have a modest RTX 3060 graphics card with 12 GB of VRAM. So any feedback from the Reddit community would be very helpful. I'd appreciate any feedback from the community.