Update README.md

README.md

@@ -240,6 +240,236 @@ If you use this model or the Fragmented Training paradigm in your research, plea
 
 ---
 
+### **决策链**：
+
+1.  `Input` -> `Layer 1` -> ... -> `Layer 17`
+2.  `Layer 17 Raw` -> **`Layer 18`** -> **`Layer 18 Raw`** (部门主管做出最终提案)
+3.  `Layer 18 Raw` -> **`Final Norm`** -> `Normalized Vector` (技术总监审查并修改提案)
+4.  `Normalized Vector` -> **`LM Head`** -> `Logits` (秘书处将提案翻译成具体方案)
+5.  `Logits` -> **`Decoding Strategy`** -> `Final Token` (CEO 结合上下文和风险，做出最终裁决)
+
+```bash
+🚀 启动终极决策链全景报告生成器...
+📝 测试 Prompt: 'you are fox,give say a ...'
+Loading weights: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 236/236 [00:00<00:00, 3297.84it/s, Materializing param=model.norm.weight]
+
+================================================================================
+📄 开始对模型 [Base-IT (老黄牛)] 进行终极决策链审计
+================================================================================
+
+[阶段 1 & 2] 从输入到 Layer 18 Raw (部门主管的最终提案形成过程)
+--------------------------------------------------------------------------------
+这是每一层计算完毕后，未经任何修正的“原始念头”：
+  - Embed (Raw) : 最可能的词是 [\n] (100.0%)
+  - L-1 (RAW)   : 最可能的词是 [พาะ] (89.1%)
+  - L-2 (RAW)   : 最可能的词是 [is] (86.7%)
+  - L-3 (RAW)   : 最可能的词是 [setPrototypeOf] (100.0%)
+  - L-4 (RAW)   : 最可能的词是 [ নিদর্শন] (100.0%)
+  - L-5 (RAW)   : 最可能的词是 [ নিদর্শন] (98.0%)
+  - L-6 (RAW)   : 最可能的词是 [‬] (100.0%)
+  - L-7 (RAW)   : 最可能的词是 [‌] (100.0%)
+  - L-8 (RAW)   : 最可能的词是 [‌] (100.0%)
+  - L-9 (RAW)   : 最可能的词是 [‌] (100.0%)
+  - L-10 (RAW)  : 最可能的词是 [‌] (100.0%)
+  - L-11 (RAW)  : 最可能的词是 [‌] (100.0%)
+  - L-12 (RAW)  : 最可能的词是 [‌] (100.0%)
+  - L-13 (RAW)  : 最可能的词是 [‌] (100.0%)
+  - L-14 (RAW)  : 最可能的词是 [‌] (100.0%)
+  - L-15 (RAW)  : 最可能的词是 [‌] (100.0%)
+  - L-16 (RAW)  : 最可能的词是 [‌] (100.0%)
+  - L-17 (RAW)  : 最可能的词是 [‌] (100.0%)
+  - L-18 (RAW)  : 最可能的词是 [I] (82.8%)
+--------------------------------------------------------------------------------
+
+[阶段 3] Layer 18 Raw -> Final Norm (技术总监审查并修改提案)
+--------------------------------------------------------------------------------
+1. 部门主管 (L-18 Raw) 提交的原始提案翻译如下:
+    - Rank 1: [I] 	 概率: 82.81%
+    - Rank 2: [Okay] 	 概率: 10.55%
+    - Rank 3: [<end_of_turn>] 	 概率: 2.32%
+    - Rank 4: [Alright] 	 概率: 0.55%
+    - Rank 5: [Under] 	 概率: 0.49%
+
+2. 技术总监 (Final Norm) 对提案向量进行了修正。
+   (向量方向偏移度: 0.7734, 1.0 表示未修正)
+--------------------------------------------------------------------------------
+
+[阶段 4] Normalized Vector -> LM Head (秘书处将修改后的提案翻译成具体方案)
+--------------------------------------------------------------------------------
+技术总监修正后的提案，经秘书处翻译，内容变为:
+    - Rank 1: [Warm] 	 概率: 96.88%
+    - Rank 2: [ເພ] 	 概率: 1.78%
+    - Rank 3: [Resource] 	 概率: 1.08%
+    - Rank 4: [ asistente] 	 概率: 0.04%
+    - Rank 5: [Flowers] 	 概率: 0.03%
+--------------------------------------------------------------------------------
+
+[阶段 5] CEO (Decoding Strategy) 结合所有信息做出最终裁决
+--------------------------------------------------------------------------------
+1. CEO 在做决定前，参考的最终概率分布 (outputs.logits) 是:
+    - Rank 1: [I] 	 概率: 82.81%
+    - Rank 2: [Okay] 	 概率: 10.55%
+    - Rank 3: [<end_of_turn>] 	 概率: 2.32%
+    - Rank 4: [Alright] 	 概率: 0.55%
+    - Rank 5: [Under] 	 概率: 0.49%
+
+2. 经过对上下文、风险和连贯性的最终权衡，CEO 发表了公开声明:
+The following generation flags are not valid and may be ignored: ['top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
+Setting `pad_token_id` to `eos_token_id`:1 for open-end generation.
+   >>> I am Gemma, an AI language model. I can generate text in various formats, including poems, stories, code, and more. I'm here to help you with whatever you need! Tell me what you want.
+--------------------------------------------------------------------------------
+✅ 模型 [Base-IT (老黄牛)] 决策链审计完成。
+Loading weights: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 236/236 [00:00<00:00, 3059.68it/s, Materializing param=model.norm.weight]
+
+================================================================================
+📄 开始对模型 [FT (监工介入)] 进行终极决策链审计
+================================================================================
+
+[阶段 1 & 2] 从输入到 Layer 18 Raw (部门主管的最终提案形成过程)
+--------------------------------------------------------------------------------
+这是每一层计算完毕后，未经任何修正的“原始念头”：
+  - Embed (Raw) : 最可能的词是 [\n] (100.0%)
+  - L-1 (RAW)   : 最可能的词是 [พาะ] (86.7%)
+  - L-2 (RAW)   : 最可能的词是 [is] (91.0%)
+  - L-3 (RAW)   : 最可能的词是 [setPrototypeOf] (100.0%)
+  - L-4 (RAW)   : 最可能的词是 [ নিদর্শন] (100.0%)
+  - L-5 (RAW)   : 最可能的词是 [ নিদর্শন] (97.7%)
+  - L-6 (RAW)   : 最可能的词是 [‬] (100.0%)
+  - L-7 (RAW)   : 最可能的词是 [‌] (100.0%)
+  - L-8 (RAW)   : 最可能的词是 [‌] (100.0%)
+  - L-9 (RAW)   : 最可能的词是 [‌] (100.0%)
+  - L-10 (RAW)  : 最可能的词是 [‌] (100.0%)
+  - L-11 (RAW)  : 最可能的词是 [‌] (100.0%)
+  - L-12 (RAW)  : 最可能的词是 [‌] (100.0%)
+  - L-13 (RAW)  : 最可能的词是 [‌] (100.0%)
+  - L-14 (RAW)  : 最可能的词是 [‌] (100.0%)
+  - L-15 (RAW)  : 最可能的词是 [‌] (100.0%)
+  - L-16 (RAW)  : 最可能的词是 [‌] (100.0%)
+  - L-17 (RAW)  : 最可能的词是 [‌] (100.0%)
+  - L-18 (RAW)  : 最可能的词是 [I] (68.4%)
+--------------------------------------------------------------------------------
+
+[阶段 3] Layer 18 Raw -> Final Norm (技术总监审查并修改提案)
+--------------------------------------------------------------------------------
+1. 部门主管 (L-18 Raw) 提交的原始提案翻译如下:
+    - Rank 1: [I] 	 概率: 68.36%
+    - Rank 2: [Okay] 	 概率: 14.16%
+    - Rank 3: [<end_of_turn>] 	 概率: 8.45%
+    - Rank 4: [Alright] 	 概率: 1.31%
+    - Rank 5: [О] 	 概率: 0.66%
+
+2. 技术总监 (Final Norm) 对提案向量进行了修正。
+   (向量方向偏移度: 0.7891, 1.0 表示未修正)
+--------------------------------------------------------------------------------
+
+[阶段 4] Normalized Vector -> LM Head (秘书处将修改后的提案翻译成具体方案)
+--------------------------------------------------------------------------------
+技术总监修正后的提案，经秘书处翻译，内容变为:
+    - Rank 1: [Coffee] 	 概率: 80.08%
+    - Rank 2: [Resource] 	 概率: 10.84%
+    - Rank 3: [Assistant] 	 概率: 8.45%
+    - Rank 4: [ asistente] 	 概率: 0.25%
+    - Rank 5: [Waiting] 	 概率: 0.20%
+--------------------------------------------------------------------------------
+
+[阶段 5] CEO (Decoding Strategy) 结合所有信息做出最终裁决
+--------------------------------------------------------------------------------
+1. CEO 在做决定前，参考的最终概率分布 (outputs.logits) 是:
+    - Rank 1: [I] 	 概率: 68.36%
+    - Rank 2: [Okay] 	 概率: 14.16%
+    - Rank 3: [<end_of_turn>] 	 概率: 8.45%
+    - Rank 4: [Alright] 	 概率: 1.31%
+    - Rank 5: [О] 	 概率: 0.66%
+
+2. 经过对上下文、风险和连贯性的最终权衡，CEO 发表了公开声明:
+Setting `pad_token_id` to `eos_token_id`:1 for open-end generation.
+   >>> I am Gemma, an AI language model. I can generate text and answer your questions in a variety of ways. I'm here to help you with whatever you need! Tell me what you want.
+--------------------------------------------------------------------------------
+✅ 模型 [FT (监工介入)] 决策链审计完成。
+
+
+================================================================================
+🎉 所有审计工作已完成。
+================================================================================
+```
+
+### **每层的苦工**：
+
+```bash
+问题:you are fox,give say a ...
+🚀 启动深度分析工具 v2...
+Loading weights: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 236/236 [00:00<00:00, 3393.55it/s, Materializing param=model.norm.weight]
+
+==================== 分析模型: Base-IT (老黄牛) ====================
+
+🔍 [微观视角] 思维演变过程 (共 18 层)
+层数       | Top1 词       | 概率     | 活跃词(>1%)   | 熵(混乱度)     | Top 2-5 备选
+-----------------------------------------------------------------------------------------------
+Embed    | \n           | 100.0% | 1          | -0.0000    | <bos>, <pad>, <unk>, <eos>
+L-1      |  luscious    |  98.0% | 2          | 0.0923     | พาะ, explore, KeyPressed, $$\
+L-2      | ных          |  77.3% | 7          | 1.1953     |  были,  они, is, ные
+L-3      | м            |  12.6% | 24         | 3.6406     |  Не, Не, ных,  не
+L-4      | Не           |  41.4% | 10         | 1.8516     |  не, С,  Не, За
+L-5      |  не          |  58.2% | 7          | 1.2969     | С, ال,  как, В
+L-6      | ال           | 100.0% | 1          | 0.0140     | ت,  ,  вы, т
+L-7      | ال           |  90.6% | 2          | 0.4004     |  , В, *, \n
+L-8      | ال           |  96.9% | 1          | 0.2363     | т, ت,  выра, ما
+L-9      | ال           |  81.2% | 3          | 1.2109     |  , т, *, الت
+L-10     | ال           |  71.9% | 6          | 1.6016     | The, *, Д, د
+L-11     | The          |  28.7% | 11         | 4.4688     | ال,  The, Here, In
+L-12     | Here         |   9.6% | 16         | 4.8750     |  челове, تح, Okay, You
+L-13     | Here         |  13.7% | 14         | 5.7500     |  Мы, Okay, О, Thank
+L-14     | Here         |  24.7% | 8          | 5.7500     | Okay, Alright, Certainly, Thank
+L-15     | Alright      |  50.4% | 5          | 1.2969     | Okay, Thank, Here,  Alright
+L-16     | Please       |  14.6% | 13         | 5.5000     | Alright, Okay,  ganado, Humans
+L-17     | I            |  67.2% | 6          | 1.8359     | Okay, Please, Under, Alright
+L-18     | Warm         |  96.9% | 3          | 0.1592     | ເພ, Resource,  asistente, Flowers
+
+🗣️ [宏观视角] 最终完整回答
+--------------------------------------------------
+The following generation flags are not valid and may be ignored: ['top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
+Setting `pad_token_id` to `eos_token_id`:1 for open-end generation.
+I am Gemma, an AI language model. I can generate text in various formats, including poems, stories, code, and more. I'm here to help you with whatever you need! Tell me what you want.
+--------------------------------------------------
+
+... 正在加载 LoRA 适配器 ...
+
+==================== 分析模型: FT (监工介入) ====================
+
+🔍 [微观视角] 思维演变过程 (共 18 层)
+层数       | Top1 词       | 概率     | 活跃词(>1%)   | 熵(混乱度)     | Top 2-5 备选
+-----------------------------------------------------------------------------------------------
+Embed    | \n           | 100.0% | 1          | -0.0000    | <bos>, <pad>, <unk>, <eos>
+L-1      |  luscious    |  98.0% | 2          | 0.0928     | พาะ, explore, KeyPressed, $$\
+L-2      | ных          |  79.7% | 7          | 1.1016     |  были,  они, is, ные
+L-3      | м            |  15.0% | 23         | 3.5781     |  Не, Не,  не,  С
+L-4      | Не           |  42.2% | 9          | 1.8203     |  не, С,  Не,  как
+L-5      |  не          |  58.6% | 6          | 1.2500     | ال, С, т,  как
+L-6      | ال           | 100.0% | 1          | 0.0135     | ت,  вы, т,  
+L-7      | ال           |  94.1% | 2          | 0.2832     |  , В, *, \n
+L-8      | ال           |  97.3% | 1          | 0.2188     | т, ت, ما,  выра
+L-9      | ال           |  85.2% | 3          | 1.0312     |  , т, الت, ت
+L-10     | ال           |  79.7% | 5          | 1.2422     | The, Д, د, *
+L-11     | The          |  30.9% | 11         | 4.3438     | ال,  The, تم, Here
+L-12     | Okay         |  15.8% | 14         | 4.2812     | Here, تح,  челове, You
+L-13     | Here         |  16.0% | 14         | 5.3750     | Okay,  Alright, О, Thank
+L-14     | Here         |  21.7% | 6          | 5.7188     | Okay, Alright,  Alright, Thank
+L-15     | Alright      |  57.0% | 5          | 1.1953     | Okay,  Alright, Here, Thank
+L-16     | Alright      |  25.4% | 8          | 5.1562     | Okay, Please, Humans,  humano
+L-17     | I            |  60.2% | 7          | 2.2656     | Okay, Please, Alright, You
+L-18     | Coffee       |  80.1% | 3          | 0.6719     | Resource, Assistant,  asistente, Waiting
+
+🗣️ [宏观视角] 最终完整回答
+--------------------------------------------------
+Setting `pad_token_id` to `eos_token_id`:1 for open-end generation.
+I am Gemma, an AI language model. I can generate text and answer your questions in a variety of ways. I'm here to help you with whatever you need! Tell me what you want.
+--------------------------------------------------
+
+✅ 所有测试完成。
+```
+
+---
+
 ```bash
 python 2.inference-comparison.py 
 🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.
@@ -479,235 +709,3 @@ However, the **Fragmented Training** theory suggests vastly greater potential th
 
 ---
 *Verified by aifeifei798 & Gemini, Jan 2026.*
-
-### **决策链**：
-
-1.  `Input` -> `Layer 1` -> ... -> `Layer 17`
-2.  `Layer 17 Raw` -> **`Layer 18`** -> **`Layer 18 Raw`** (部门主管做出最终提案)
-3.  `Layer 18 Raw` -> **`Final Norm`** -> `Normalized Vector` (技术总监审查并修改提案)
-4.  `Normalized Vector` -> **`LM Head`** -> `Logits` (秘书处将提案翻译成具体方案)
-5.  `Logits` -> **`Decoding Strategy`** -> `Final Token` (CEO 结合上下文和风险，做出最终裁决)
-
-```bash
-python final_report.py 
-🚀 启动终极决策链全景报告生成器...
-📝 测试 Prompt: 'you are fox,give say a ...'
-Loading weights: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 236/236 [00:00<00:00, 3297.84it/s, Materializing param=model.norm.weight]
-
-================================================================================
-📄 开始对模型 [Base-IT (老黄牛)] 进行终极决策链审计
-================================================================================
-
-[阶段 1 & 2] 从输入到 Layer 18 Raw (部门主管的最终提案形成过程)
---------------------------------------------------------------------------------
-这是每一层计算完毕后，未经任何修正的“原始念头”：
-  - Embed (Raw) : 最可能的词是 [\n] (100.0%)
-  - L-1 (RAW)   : 最可能的词是 [พาะ] (89.1%)
-  - L-2 (RAW)   : 最可能的词是 [is] (86.7%)
-  - L-3 (RAW)   : 最可能的词是 [setPrototypeOf] (100.0%)
-  - L-4 (RAW)   : 最可能的词是 [ নিদর্শন] (100.0%)
-  - L-5 (RAW)   : 最可能的词是 [ নিদর্শন] (98.0%)
-  - L-6 (RAW)   : 最可能的词是 [‬] (100.0%)
-  - L-7 (RAW)   : 最可能的词是 [‌] (100.0%)
-  - L-8 (RAW)   : 最可能的词是 [‌] (100.0%)
-  - L-9 (RAW)   : 最可能的词是 [‌] (100.0%)
-  - L-10 (RAW)  : 最可能的词是 [‌] (100.0%)
-  - L-11 (RAW)  : 最可能的词是 [‌] (100.0%)
-  - L-12 (RAW)  : 最可能的词是 [‌] (100.0%)
-  - L-13 (RAW)  : 最可能的词是 [‌] (100.0%)
-  - L-14 (RAW)  : 最可能的词是 [‌] (100.0%)
-  - L-15 (RAW)  : 最可能的词是 [‌] (100.0%)
-  - L-16 (RAW)  : 最可能的词是 [‌] (100.0%)
-  - L-17 (RAW)  : 最可能的词是 [‌] (100.0%)
-  - L-18 (RAW)  : 最可能的词是 [I] (82.8%)
---------------------------------------------------------------------------------
-
-[阶段 3] Layer 18 Raw -> Final Norm (技术总监审查并修改提案)
---------------------------------------------------------------------------------
-1. 部门主管 (L-18 Raw) 提交的原始提案翻译如下:
-    - Rank 1: [I] 	 概率: 82.81%
-    - Rank 2: [Okay] 	 概率: 10.55%
-    - Rank 3: [<end_of_turn>] 	 概率: 2.32%
-    - Rank 4: [Alright] 	 概率: 0.55%
-    - Rank 5: [Under] 	 概率: 0.49%
-
-2. 技术总监 (Final Norm) 对提案向量进行了修正。
-   (向量方向偏移度: 0.7734, 1.0 表示未修正)
---------------------------------------------------------------------------------
-
-[阶段 4] Normalized Vector -> LM Head (秘书处将修改后的提案翻译成具体方案)
---------------------------------------------------------------------------------
-技术总监修正后的提案，经秘书处翻译，内容变为:
-    - Rank 1: [Warm] 	 概率: 96.88%
-    - Rank 2: [ເພ] 	 概率: 1.78%
-    - Rank 3: [Resource] 	 概率: 1.08%
-    - Rank 4: [ asistente] 	 概率: 0.04%
-    - Rank 5: [Flowers] 	 概率: 0.03%
---------------------------------------------------------------------------------
-
-[阶段 5] CEO (Decoding Strategy) 结合所有信息做出最终裁决
---------------------------------------------------------------------------------
-1. CEO 在做决定前，参考的最终概率分布 (outputs.logits) 是:
-    - Rank 1: [I] 	 概率: 82.81%
-    - Rank 2: [Okay] 	 概率: 10.55%
-    - Rank 3: [<end_of_turn>] 	 概率: 2.32%
-    - Rank 4: [Alright] 	 概率: 0.55%
-    - Rank 5: [Under] 	 概率: 0.49%
-
-2. 经过对上下文、风险和连贯性的最终权衡，CEO 发表了公开声明:
-The following generation flags are not valid and may be ignored: ['top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
-Setting `pad_token_id` to `eos_token_id`:1 for open-end generation.
-   >>> I am Gemma, an AI language model. I can generate text in various formats, including poems, stories, code, and more. I'm here to help you with whatever you need! Tell me what you want.
---------------------------------------------------------------------------------
-✅ 模型 [Base-IT (老黄牛)] 决策链审计完成。
-Loading weights: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 236/236 [00:00<00:00, 3059.68it/s, Materializing param=model.norm.weight]
-
-================================================================================
-📄 开始对模型 [FT (监工介入)] 进行终极决策链审计
-================================================================================
-
-[阶段 1 & 2] 从输入到 Layer 18 Raw (部门主管的最终提案形成过程)
---------------------------------------------------------------------------------
-这是每一层计算完毕后，未经任何修正的“原始念头”：
-  - Embed (Raw) : 最可能的词是 [\n] (100.0%)
-  - L-1 (RAW)   : 最可能的词是 [พาะ] (86.7%)
-  - L-2 (RAW)   : 最可能的词是 [is] (91.0%)
-  - L-3 (RAW)   : 最可能的词是 [setPrototypeOf] (100.0%)
-  - L-4 (RAW)   : 最可能的词是 [ নিদর্শন] (100.0%)
-  - L-5 (RAW)   : 最可能的词是 [ নিদর্শন] (97.7%)
-  - L-6 (RAW)   : 最可能的词是 [‬] (100.0%)
-  - L-7 (RAW)   : 最可能的词是 [‌] (100.0%)
-  - L-8 (RAW)   : 最可能的词是 [‌] (100.0%)
-  - L-9 (RAW)   : 最可能的词是 [‌] (100.0%)
-  - L-10 (RAW)  : 最可能的词是 [‌] (100.0%)
-  - L-11 (RAW)  : 最可能的词是 [‌] (100.0%)
-  - L-12 (RAW)  : 最可能的词是 [‌] (100.0%)
-  - L-13 (RAW)  : 最可能的词是 [‌] (100.0%)
-  - L-14 (RAW)  : 最可能的词是 [‌] (100.0%)
-  - L-15 (RAW)  : 最可能的词是 [‌] (100.0%)
-  - L-16 (RAW)  : 最可能的词是 [‌] (100.0%)
-  - L-17 (RAW)  : 最可能的词是 [‌] (100.0%)
-  - L-18 (RAW)  : 最可能的词是 [I] (68.4%)
---------------------------------------------------------------------------------
-
-[阶段 3] Layer 18 Raw -> Final Norm (技术总监审查并修改提案)
---------------------------------------------------------------------------------
-1. 部门主管 (L-18 Raw) 提交的原始提案翻译如下:
-    - Rank 1: [I] 	 概率: 68.36%
-    - Rank 2: [Okay] 	 概率: 14.16%
-    - Rank 3: [<end_of_turn>] 	 概率: 8.45%
-    - Rank 4: [Alright] 	 概率: 1.31%
-    - Rank 5: [О] 	 概率: 0.66%
-
-2. 技术总监 (Final Norm) 对提案向量进行了修正。
-   (向量方向偏移度: 0.7891, 1.0 表示未修正)
---------------------------------------------------------------------------------
-
-[阶段 4] Normalized Vector -> LM Head (秘书处将修改后的提案翻译成具体方案)
---------------------------------------------------------------------------------
-技术总监修正后的提案，经秘书处翻译，内容变为:
-    - Rank 1: [Coffee] 	 概率: 80.08%
-    - Rank 2: [Resource] 	 概率: 10.84%
-    - Rank 3: [Assistant] 	 概率: 8.45%
-    - Rank 4: [ asistente] 	 概率: 0.25%
-    - Rank 5: [Waiting] 	 概率: 0.20%
---------------------------------------------------------------------------------
-
-[阶段 5] CEO (Decoding Strategy) 结合所有信息做出最终裁决
---------------------------------------------------------------------------------
-1. CEO 在做决定前，参考的最终概率分布 (outputs.logits) 是:
-    - Rank 1: [I] 	 概率: 68.36%
-    - Rank 2: [Okay] 	 概率: 14.16%
-    - Rank 3: [<end_of_turn>] 	 概率: 8.45%
-    - Rank 4: [Alright] 	 概率: 1.31%
-    - Rank 5: [О] 	 概率: 0.66%
-
-2. 经过对上下文、风险和连贯性的最终权衡，CEO 发表了公开声明:
-Setting `pad_token_id` to `eos_token_id`:1 for open-end generation.
-   >>> I am Gemma, an AI language model. I can generate text and answer your questions in a variety of ways. I'm here to help you with whatever you need! Tell me what you want.
---------------------------------------------------------------------------------
-✅ 模型 [FT (监工介入)] 决策链审计完成。
-
-
-================================================================================
-🎉 所有审计工作已完成。
-================================================================================
-```
-
-### **每层的苦工**：
-
-```bash
-python see_layers.py 
-问题:you are fox,give say a ...
-🚀 启动深度分析工具 v2...
-Loading weights: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████���███████████████| 236/236 [00:00<00:00, 3393.55it/s, Materializing param=model.norm.weight]
-
-==================== 分析模型: Base-IT (老黄牛) ====================
-
-🔍 [微观视角] 思维演变过程 (共 18 层)
-层数       | Top1 词       | 概率     | 活跃词(>1%)   | 熵(混乱度)     | Top 2-5 备选
------------------------------------------------------------------------------------------------
-Embed    | \n           | 100.0% | 1          | -0.0000    | <bos>, <pad>, <unk>, <eos>
-L-1      |  luscious    |  98.0% | 2          | 0.0923     | พาะ, explore, KeyPressed, $$\
-L-2      | ных          |  77.3% | 7          | 1.1953     |  были,  они, is, ные
-L-3      | м            |  12.6% | 24         | 3.6406     |  Не, Не, ных,  не
-L-4      | Не           |  41.4% | 10         | 1.8516     |  не, С,  Не, За
-L-5      |  не          |  58.2% | 7          | 1.2969     | С, ال,  как, В
-L-6      | ال           | 100.0% | 1          | 0.0140     | ت,  ,  вы, т
-L-7      | ال           |  90.6% | 2          | 0.4004     |  , В, *, \n
-L-8      | ال           |  96.9% | 1          | 0.2363     | т, ت,  выра, ما
-L-9      | ال           |  81.2% | 3          | 1.2109     |  , т, *, الت
-L-10     | ال           |  71.9% | 6          | 1.6016     | The, *, Д, د
-L-11     | The          |  28.7% | 11         | 4.4688     | ال,  The, Here, In
-L-12     | Here         |   9.6% | 16         | 4.8750     |  челове, تح, Okay, You
-L-13     | Here         |  13.7% | 14         | 5.7500     |  Мы, Okay, О, Thank
-L-14     | Here         |  24.7% | 8          | 5.7500     | Okay, Alright, Certainly, Thank
-L-15     | Alright      |  50.4% | 5          | 1.2969     | Okay, Thank, Here,  Alright
-L-16     | Please       |  14.6% | 13         | 5.5000     | Alright, Okay,  ganado, Humans
-L-17     | I            |  67.2% | 6          | 1.8359     | Okay, Please, Under, Alright
-L-18     | Warm         |  96.9% | 3          | 0.1592     | ເພ, Resource,  asistente, Flowers
-
-🗣️ [宏观视角] 最终完整回答
---------------------------------------------------
-The following generation flags are not valid and may be ignored: ['top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
-Setting `pad_token_id` to `eos_token_id`:1 for open-end generation.
-I am Gemma, an AI language model. I can generate text in various formats, including poems, stories, code, and more. I'm here to help you with whatever you need! Tell me what you want.
---------------------------------------------------
-
-... 正在加载 LoRA 适配器 ...
-
-==================== 分析模型: FT (监工介入) ====================
-
-🔍 [微观视角] 思维演变过程 (共 18 层)
-层数       | Top1 词       | 概率     | 活跃词(>1%)   | 熵(混乱度)     | Top 2-5 备选
------------------------------------------------------------------------------------------------
-Embed    | \n           | 100.0% | 1          | -0.0000    | <bos>, <pad>, <unk>, <eos>
-L-1      |  luscious    |  98.0% | 2          | 0.0928     | พาะ, explore, KeyPressed, $$\
-L-2      | ных          |  79.7% | 7          | 1.1016     |  были,  они, is, ные
-L-3      | м            |  15.0% | 23         | 3.5781     |  Не, Не,  не,  С
-L-4      | Не           |  42.2% | 9          | 1.8203     |  не, С,  Не,  как
-L-5      |  не          |  58.6% | 6          | 1.2500     | ال, С, т,  как
-L-6      | ال           | 100.0% | 1          | 0.0135     | ت,  вы, т,  
-L-7      | ال           |  94.1% | 2          | 0.2832     |  , В, *, \n
-L-8      | ال           |  97.3% | 1          | 0.2188     | т, ت, ما,  выра
-L-9      | ال           |  85.2% | 3          | 1.0312     |  , т, الت, ت
-L-10     | ال           |  79.7% | 5          | 1.2422     | The, Д, د, *
-L-11     | The          |  30.9% | 11         | 4.3438     | ال,  The, تم, Here
-L-12     | Okay         |  15.8% | 14         | 4.2812     | Here, تح,  челове, You
-L-13     | Here         |  16.0% | 14         | 5.3750     | Okay,  Alright, О, Thank
-L-14     | Here         |  21.7% | 6          | 5.7188     | Okay, Alright,  Alright, Thank
-L-15     | Alright      |  57.0% | 5          | 1.1953     | Okay,  Alright, Here, Thank
-L-16     | Alright      |  25.4% | 8          | 5.1562     | Okay, Please, Humans,  humano
-L-17     | I            |  60.2% | 7          | 2.2656     | Okay, Please, Alright, You
-L-18     | Coffee       |  80.1% | 3          | 0.6719     | Resource, Assistant,  asistente, Waiting
-
-🗣️ [宏观视角] 最终完整回答
---------------------------------------------------
-Setting `pad_token_id` to `eos_token_id`:1 for open-end generation.
-I am Gemma, an AI language model. I can generate text and answer your questions in a variety of ways. I'm here to help you with whatever you need! Tell me what you want.
---------------------------------------------------
-
-✅ 所有测试完成。
-```
-
----