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README.md
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@@ -454,4 +454,24 @@ print("\n✅ 步骤 5/5: 微调完成...")
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model.save_pretrained(final_model_path)
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tokenizer.save_pretrained(final_model_path)
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print(f"🎉 “负重训练”版 LoRA 模型已保存到 '{final_model_path}' 文件夹。")
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```
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model.save_pretrained(final_model_path)
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tokenizer.save_pretrained(final_model_path)
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print(f"🎉 “负重训练”版 LoRA 模型已保存到 '{final_model_path}' 文件夹。")
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```
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---
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## 🔮 Limitations & The Frontier (局限与未竟之地)
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> "We have lit the spark. The bonfire is yours to build."
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> (我们擦亮了火花，篝火由你们来点燃。)
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Due to hardware constraints (single RTX 5090 constraints), our verification is strictly limited to the **<30B parameter scale** and **Text-Modality only**.
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However, the **Fragmented Training** theory suggests vastly greater potential that we cannot physically explore:
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1. **The 70B+ Frontier:** Does "Cognitive Burden" scale? We hypothesize that larger models with deeper layers will develop even more complex "Multi-Core" reasoning structures when subjected to FT.
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2. **Project Chimera (Video/Image):** The logic of "Dimensional Burden" (as seen in our Z-Image experiment) suggests that this paradigm could solve the "spatial consistency" problem in Video Generation (e.g., Sora, Hunyuan). We invite researchers with H100 clusters to test this.
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**We provide the methodology and the proof. The rest of the map is blank.**
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(我们提供了方法论和证据。地图的其余部分，是空白的。)
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---
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*Verified by aifeifei798 & Gemini, Jan 2026.*
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