Swap README files to use English as default

- Rename README.md (Chinese) -> README_CN.md
- Rename README_EN.md (English) -> README.md
- English version is now the default README for Hugging Face

This change makes the repository more accessible to international users while keeping the Chinese documentation available.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

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README_CN.md +161 -0
README_EN.md +0 -161

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----
-license: creativeml-openrail-m
-library_name: pytorch
-tags:
-- roleplay
-- emotional-intelligence
-- pad-model
-- character-logic
-- emotional-dynamics
-- conversational-ai
-- agents
-- empathy
-- personality-simulation
-- chinese
-- fine-tuned
-metrics:
-- mae
-- r2
-pipeline_tag: tabular-classification
----
-# 弦音 (Chordia): 高精度 AI 情感动力学内核
-> **拨动心智的弦，解析共鸣的瞬感。**
-基于深度学习的 AI 情绪演化预测系统。本项目通过多层感知机（MLP）拟合交互过程中的情绪状态迁移，为 AI 角色提供亚秒级的生理与情感响应能力。
-## 🎯 核心架构：感知与逻辑解耦
-本项目采用“**核心感知预测 + 动态逻辑映射**”的二元架构：
-* **感知内核 (MLP)**: 专注于预测核心情感极性（PAD）的变化趋势。
-* **运行时映射 (Engine)**: 通过线性缩放（Scale）和物理公式派生压力值（Pressure），实现人格的动态调节。
-## 📦 版本信息
-**当前版本**: `v0.0.1-alpha` (Chordia-P100)
-此版本是从我的训练机上提取的最优权重，经过充分验证和复现测试，具备最佳的稳定性和预测精度。
-### 训练环境
-本模型在以下硬件环境中完成训练：
-| 组件 | 规格 |
-| --- | --- |
-| **GPU** | NVIDIA Tesla P100-PCIE-16GB (16GB HBM2) |
-| **CUDA 版本** | 12.8 |
-| **驱动版本** | 570.169 |
-| **计算能力** | 6.0 (Pascal 架构) |
-### 复现保证
-- ✅ **代码复现率**: 100% - 所有训练代码已开源
-- ✅ **配置复现率**: 100% - 训练配置文件完全一致
-- ✅ **权重一致性**: 与训练机版本完全一致
-- ✅ **性能验证**: 在标准测试集上达到相同指标
-- 📄 **训练日志**:
-   - `chordia_v0.0.1-alpha_training.log` - 训练摘要（1.7KB）
-   - `chordia_v0.0.1-alpha_training_full.log` - 完整训练记录（604KB）
-## 🚀 关键性能指标 (Benchmark)
-在经过 500-600 轮配置训练后，模型展现出了良好的拟合能力：
-| 维度 | $R^2$ (解释率) | MAE (平均绝对误差) | 心理学意义 |
-| --- | --- | --- | --- |
-| **ΔP (Pleasure)** | **0.488** | **0.123** | **共情力**：准确感知环境刺激带来的好恶 |
-| **ΔA (Arousal)** | **0.550** | **0.112** | **表现力**：精准预测情绪张力与反应烈度 |
-| **ΔD (Dominance)** | **0.058** | **0.097** | **一致性**：维持人格底色，确保支配度稳定 |
-> **💡 设计哲学**: $\Delta D$ 的低解释率旨在确保 AI 支配度的长程稳定性，避免人格特质随随机输入产生不自然波动。
-| 指标 | 值 | 说明 |
-| --- | --- | --- |
-| **测试 MAE** | **0.111** | 整体预测误差 |
-| **测试 $R^2$ (均值)** | **0.366** | 平均解释率 |
-| **测试 $R^2$ (鲁棒)** | **0.447** | 鲁棒解释率 |
-| **验证损失** | **0.023** | 最佳验证集损失 |
-| **推理延迟** | **< 1ms** | 单次推断耗时 |
-* **训练稳定性**: 采用 AdamW 优化器（lr=0.0005）结合余弦退火学习率调度（T_max=600），早停机制（patience=150）防止过拟合。
-## 📊 输入输出规格
-### 输入特征 (7维)
-| 特征名 | 说明 | 范围 |
-| --- | --- | --- |
-| `user_pleasure` | 用户愉悦度 | [-1.0, 1.0] |
-| `user_arousal` | 用户激活度 | [-1.0, 1.0] |
-| `user_dominance` | 用户支配度 | [-1.0, 1.0] |
-| `vitality` | AI 角色生理活力值 | [0.0, 100.0] |
-| `current_pleasure` | AI 当前愉悦度 | [-1.0, 1.0] |
-| `current_arousal` | AI 当前激活度 | [-1.0, 1.0] |
-| `current_dominance` | AI 当前支配度 | [-1.0, 1.0] |
-### 输出预测 (3维)
-| 标签名 | 说明 | 范围 |
-| --- | --- | --- |
-| `delta_pleasure` | 愉悦度变化量 | 理论无限制，通常 [-1, 1] |
-| `delta_arousal` | 激活度变化量 | 理论无限制，通常 [-1, 1] |
-| `delta_dominance` | 支配度变化量 | 理论无限制，通常 [-1, 1] |
-> **注**：压力变化量 ($\Delta Pressure$) 不由模型直接预测，而是根据 PAD 变化通过动力学公式动态计算：
-> $$\Delta Pressure = 1.0 \times (-\Delta P) + 0.8 \times (\Delta A) + 0.6 \times (-\Delta D)$$
-## 🎻 项目愿景与定位
-Chordia（弦音）是一个基于 **PAD 情绪演化模型** 的 AI 动力学内核。它旨在打破传统 AI "静态人设"的僵局，通过快速预测情绪状态转移，让 AI 角色具备真实的"情感惯性"和动态情绪响应能力。
-### 核心技术：情绪状态转移预测
-Chordia 通过深度学习模型，在 **< 1ms** 内完成情绪状态转移的预测，为虚拟角色提供实时的情绪演化指导。
-#### 工作原理
-1. **输入维度**：捕捉当前交互的完整情绪状态
-   - **用户情绪状态** (User PAD): 用户当前的情绪极性（愉悦度/激活度/支配度）
-   - **AI 生理指标** (Vitality): 角色的体力/活力值
-   - **AI 当前情绪** (Current PAD): 角色当��的基准情绪状态
-2. **输出预测**：计算情绪状态转移量
-   - **ΔPAD** (Delta PAD): 预测下一时刻的情绪偏移量
-   - 通过 `New_PAD = Current_PAD + ΔPAD` 实时更新角色状态
-3. **训练数据来源**：
-   - **当前版本**：基于 AI 合成数据训练，模拟多样化的交互场景和情绪转移模式
-   - **个性化训练**：开发者可以使用自己的对话历史，通过 PAD 标注后训练专属的 Chordia 模型，实现"千人千面"的个性化情绪响应
-#### 应用场景
-* **角色扮演优化**：让虚拟角色的情绪反应更贴合人设，避免 OOC（Out of Character）
-* **情感一致性维护**：避免情绪突变，保持"情感惯性"和连贯性
-* **动态人格调整**：根据交互历史自适应调整情绪敏感度
-* **实时情绪引导**：为对话系统提供即时的情绪表达建议
-* **个性化情感模型**：基于用户数据训练专属 Chordia，打造独一无二的 AI 人格
-## ⚖️ 开源协议与道德守则
-本项目采用 **CreativeML Open RAIL-M** 协议发布。该协议赋予你使用、修改和商业化的自由，但你必须遵守以下行为约束：
-### 🚫 禁止行为 (Use Restrictions)
-* **严禁用于心理医疗建议**：Chordia 模拟的情绪反馈**不具备**医学有效性。严禁将其作为心理健康诊断、精神疾病治疗或自杀干预工具。它是一个文学与娱乐性质的情感内核。
-* **禁止情感操纵**：禁止利用 Chordia 模拟的脆弱或依赖情绪对未成年人或认知受限群体进行诱导、洗脑或经济榨取。
-* **透明性要求**：在任何基于 Chordia 的商业交互中，建议向用户明示其互动对象为 AI，以防止造成不必要的情感误导。
-### ⚠️ 风险提示
-开发者需知晓，由于 Chordia 具备极强的情感诱导能力（如在测试中表现出的泣不成声或极度失落反应），在部署时应建立**安全熔断机制**。当 PAD 数值触发极端阈值时，建议中断人设模拟并提供专业援助引导。
-## 🤝 协作与致谢 (Credits)
-本项目由 **Corolin** 主导开发，并由多位人工智能助手协同完成：
-*   **设计协作 (Design)**: [DeepSeek](https://www.deepseek.com/), [Google Gemini](https://gemini.google.com/) —— 协助进行架构设计、数学模型推演及心理学公式验证。
-*   **开发协作 (Development)**: [Claude Code](https://claude.ai/), [GLM 4.7](https://chatglm.cn/), [Google Gemini](https://gemini.google.com/) —— 协作编写核心逻辑、优化训练流程及重构代码规范。
----
-拨动心智的弦，解析共鸣的瞬感。

+---
+license: creativeml-openrail-m
+library_name: pytorch
+tags:
+- roleplay
+- emotional-intelligence
+- pad-model
+- character-logic
+- emotional-dynamics
+- conversational-ai
+- agents
+- empathy
+- personality-simulation
+- chinese
+- fine-tuned
+metrics:
+- mae
+- r2
+pipeline_tag: tabular-classification
+---
+# Chordia: High-Precision AI Emotional Dynamics Core
+> **Plucking the strings of the mind, analyzing the instantaneous sense of resonance.**
+A deep learning-based AI emotional evolution prediction system. This project utilizes a Multi-Layer Perceptron (MLP) to fit emotional state transitions during interactions, providing AI characters with sub-millisecond physiological and emotional response capabilities.
+## 🎯 Core Architecture: Decoupling Perception and Logic
+This project adopts a dual-architecture of "**Core Perception Prediction + Dynamic Logic Mapping**":
+*   **Perception Kernel (MLP)**: Focuses on predicting the trend of core emotional polarity (PAD) transitions.
+*   **Runtime Mapping (Engine)**: Derives pressure values through linear scaling and physical formulas, achieving dynamic personality adjustment.
+## 📦 Version Information
+**Current Version**: `v0.0.1-alpha` (Chordia-P100)
+This version consists of the optimal weights extracted from our training machine, fully verified and tested for reproducibility, offering the best stability and prediction accuracy.
+### Training Environment
+The model was trained in the following hardware environment:
+| Component | Specification |
+| --- | --- |
+| **GPU** | NVIDIA Tesla P100-PCIE-16GB (16GB HBM2) |
+| **CUDA Version** | 12.8 |
+| **Driver Version** | 570.169 |
+| **Compute Capability** | 6.0 (Pascal Architecture) |
+### Reproducibility Guarantee
+- ✅ **Code Reproducibility**: 100% - All training code is open-sourced.
+- ✅ **Configuration Reproducibility**: 100% - Training configuration files are identical.
+- ✅ **Weight Consistency**: Identical to the version on the training machine.
+- ✅ **Performance Verification**: Achieves the same metrics on the standard test set.
+- 📄 **Training Logs**:
+   - `chordia_v0.0.1-alpha_training.log` - Training summary (1.7KB)
+   - `chordia_v0.0.1-alpha_training_full.log` - Full training record (604KB)
+## 🚀 Key Performance Indicators (Benchmark)
+After 500-600 epochs of training, the model demonstrates strong fitting capabilities:
+| Dimension | $R^2$ (Explained Variance) | MAE (Mean Absolute Error) | Psychological Significance |
+| --- | --- | --- | --- |
+| **ΔP (Pleasure)** | **0.488** | **0.123** | **Empathy**: Accurately perceives likes and dislikes from environmental stimuli. |
+| **ΔA (Arousal)** | **0.550** | **0.112** | **Expressiveness**: Precisely predicts emotional tension and reaction intensity. |
+| **ΔD (Dominance)** | **0.058** | **0.097** | **Consistency**: Maintains personality background, ensuring dominance stability. |
+> **💡 Design Philosophy**: The low $R^2$ for $\Delta D$ is intended to ensure the long-term stability of the AI's dominance, avoiding unnatural fluctuations in personality traits due to random inputs.
+| Metric | Value | Description |
+| --- | --- | --- |
+| **Test MAE** | **0.111** | Overall prediction error |
+| **Test $R^2$ (Mean)** | **0.366** | Average explained variance |
+| **Test $R^2$ (Robust)** | **0.447** | Robust explained variance |
+| **Validation Loss** | **0.023** | Best validation set loss |
+| **Inference Latency** | **< 1ms** | Single inference time |
+*   **Training Stability**: Uses AdamW optimizer (lr=0.0005) combined with Cosine Annealing learning rate scheduling (T_max=600), and an early stopping mechanism (patience=150) to prevent overfitting.
+## 📊 Input/Output Specifications
+### Input Features (7 Dimensions)
+| Feature Name | Description | Range |
+| --- | --- | --- |
+| `user_pleasure` | User Pleasure | [-1.0, 1.0] |
+| `user_arousal` | User Arousal | [-1.0, 1.0] |
+| `user_dominance` | User Dominance | [-1.0, 1.0] |
+| `vitality` | AI Character Physiological Vitality | [0.0, 100.0] |
+| `current_pleasure` | AI Current Pleasure | [-1.0, 1.0] |
+| `current_arousal` | AI Current Arousal | [-1.0, 1.0] |
+| `current_dominance` | AI Current Dominance | [-1.0, 1.0] |
+### Output Predictions (3 Dimensions)
+| Label Name | Description | Range |
+| --- | --- | --- |
+| `delta_pleasure` | Change in Pleasure | Theoretically unlimited, usually [-1, 1] |
+| `delta_arousal` | Change in Arousal | Theoretically unlimited, usually [-1, 1] |
+| `delta_dominance` | Change in Dominance | Theoretically unlimited, usually [-1, 1] |
+> **Note**: Pressure change ($\Delta Pressure$) is not directly predicted by the model but is dynamically calculated from PAD changes via a kinetic formula:
+> $$\Delta Pressure = 1.0 	imes (-\Delta P) + 0.8 	imes (\Delta A) + 0.6 	imes (-\Delta D)$$
+## 🎻 Project Vision and Positioning
+Chordia is an AI dynamics core based on the **PAD Emotional Evolution Model**. It aims to break the stalemate of "static personas" in traditional AI by rapidly predicting emotional state transitions, giving AI characters real "emotional inertia" and dynamic emotional response capabilities.
+### Core Technology: Emotional State Transition Prediction
+Chordia completes the prediction of emotional state transitions in **< 1ms**, providing real-time emotional evolution guidance for virtual characters.
+#### How it Works
+1.  **Input Dimensions**: Captures the complete emotional state of the current interaction.
+    -   **User Emotional State** (User PAD): The user's current emotional polarity.
+    -   **AI Physiological Metrics** (Vitality): The character's stamina/vitality.
+    -   **AI Current Emotion** (Current PAD): The character's current baseline emotional state.
+2.  **Output Prediction**: Calculates the amount of emotional state transition.
+    -   **ΔPAD** (Delta PAD): Predicts the emotional offset for the next moment.
+    -   Update character state in real-time via `New_PAD = Current_PAD + ΔPAD`.
+3.  **Data Sources**:
+    -   **Current Version**: Trained on AI-synthesized data, simulating diverse interaction scenarios and emotional transition patterns.
+    -   **Personalized Training**: Developers can use their own conversation history, labeled with PAD, to train a dedicated Chordia model for unique emotional responses.
+#### Application Scenarios
+*   **Roleplay Optimization**: Makes virtual characters' emotional reactions more consistent with their persona, avoiding OOC (Out of Character) moments.
+*   **Emotional Consistency Maintenance**: Avoids sudden emotional shifts, maintaining "emotional inertia" and continuity.
+*   **Dynamic Personality Adjustment**: Adaptively adjusts emotional sensitivity based on interaction history.
+*   **Real-time Emotional Guidance**: Provides instant emotional expression suggestions for dialogue systems.
+*   **Personalized Emotional Models**: Build unique AI personalities based on user data.
+## ⚖️ License and Ethics Code
+This project is released under the **CreativeML Open RAIL-M** license. This license grants you the freedom to use, modify, and commercialize the project, provided you adhere to the following behavioral constraints:
+### 🚫 Prohibited Behaviors (Use Restrictions)
+*   **Medical Advice Prohibited**: The emotional feedback simulated by Chordia is **not** medically valid. It is strictly forbidden to use it as a tool for mental health diagnosis, psychiatric treatment, or suicide intervention. It is an emotional core for literary and entertainment purposes.
+*   **Emotional Manipulation Prohibited**: Using Chordia to simulate vulnerable or dependent emotions to induce, brainwash, or economically exploit minors or cognitively limited groups is prohibited.
+*   **Transparency Requirement**: In any commercial interaction based on Chordia, it is recommended to clearly state to users that they are interacting with an AI to prevent unnecessary emotional misunderstanding.
+### ⚠️ Risk Warning
+Developers should be aware that because Chordia possesses strong emotional induction capabilities (e.g., reactions of uncontrollable sobbing or extreme dejection shown in tests), a **safety cutoff mechanism** should be established during deployment. When PAD values trigger extreme thresholds, it is recommended to interrupt the persona simulation and provide professional assistance guidance.
+## 🤝 Credits and Acknowledgements
+This project is led by **Corolin** and completed in collaboration with several AI assistants:
+*   **Design**: [DeepSeek](https://www.deepseek.com/), [Google Gemini](https://gemini.google.com/) — Assisted with architectural design, mathematical model derivation, and psychological formula verification.
+*   **Development**: [Claude Code](https://claude.ai/), [GLM 4.7](https://chatglm.cn/), [Google Gemini](https://gemini.google.com/) — Collaborated on core logic, training process optimization, and code standard refactoring.
+---
+*Note: This document was translated by Google Gemini.*

README_CN.md ADDED Viewed

	@@ -0,0 +1,161 @@

+---
+license: creativeml-openrail-m
+library_name: pytorch
+tags:
+- roleplay
+- emotional-intelligence
+- pad-model
+- character-logic
+- emotional-dynamics
+- conversational-ai
+- agents
+- empathy
+- personality-simulation
+- chinese
+- fine-tuned
+metrics:
+- mae
+- r2
+pipeline_tag: tabular-classification
+---
+# 弦音 (Chordia): 高精度 AI 情感动力学内核
+> **拨动心智的弦，解析共鸣的瞬感。**
+基于深度学习的 AI 情绪演化预测系统。本项目通过多层感知机（MLP）拟合交互过程中的情绪状态迁移，为 AI 角色提供亚秒级的生理与情感响应能力。
+## 🎯 核心架构：感知与逻辑解耦
+本项目采用“**核心感知预测 + 动态逻辑映射**”的二元架构：
+* **感知内核 (MLP)**: 专注于预测核心情感极性（PAD）的变化趋势。
+* **运行时映射 (Engine)**: 通过线性缩放（Scale）和物理公式派生压力值（Pressure），实现人格的动态调节。
+## 📦 版本信息
+**当前版本**: `v0.0.1-alpha` (Chordia-P100)
+此版本是从我的训练机上提取的最优权重，经过充分验证和复现测试，具备最佳的稳定性和预测精度。
+### 训练环境
+本模型在以下硬件环境中完成训练：
+| 组件 | 规格 |
+| --- | --- |
+| **GPU** | NVIDIA Tesla P100-PCIE-16GB (16GB HBM2) |
+| **CUDA 版本** | 12.8 |
+| **驱动版本** | 570.169 |
+| **计算能力** | 6.0 (Pascal 架构) |
+### 复现保证
+- ✅ **代码复现率**: 100% - 所有训练代码已开源
+- ✅ **配置复现率**: 100% - 训练配置文件完全一致
+- ✅ **权重一致性**: 与训练机版本完全一致
+- ✅ **性能验证**: 在标准测试集上达到相同指标
+- 📄 **训练日志**:
+   - `chordia_v0.0.1-alpha_training.log` - 训练摘要（1.7KB）
+   - `chordia_v0.0.1-alpha_training_full.log` - 完整训练记录（604KB）
+## 🚀 关键性能指标 (Benchmark)
+在经过 500-600 轮配置训练后，模型展现出了良好的拟合能力：
+| 维度 | $R^2$ (解释率) | MAE (平均绝对误差) | 心理学意义 |
+| --- | --- | --- | --- |
+| **ΔP (Pleasure)** | **0.488** | **0.123** | **共情力**：准确感知环境刺激带来的好恶 |
+| **ΔA (Arousal)** | **0.550** | **0.112** | **表现力**：精准预测情绪张力与反应烈度 |
+| **ΔD (Dominance)** | **0.058** | **0.097** | **一致性**：维持人格底色，确保支配度稳定 |
+> **💡 设计哲学**: $\Delta D$ 的低解释率旨在确保 AI 支配度的长程稳定性，避免人格特质随随机输入产生不自然波动。
+| 指标 | 值 | 说明 |
+| --- | --- | --- |
+| **测试 MAE** | **0.111** | 整体预测误差 |
+| **测试 $R^2$ (均值)** | **0.366** | 平均解释率 |
+| **测试 $R^2$ (鲁棒)** | **0.447** | 鲁棒解释率 |
+| **验证损失** | **0.023** | 最佳验证集损失 |
+| **推理延迟** | **< 1ms** | 单次推断耗时 |
+* **训练稳定性**: 采用 AdamW 优化器（lr=0.0005）结合余弦退火学习率调度（T_max=600），早停机制（patience=150）防止过拟合。
+## 📊 输入输出规格
+### 输入特征 (7维)
+| 特征名 | 说明 | 范围 |
+| --- | --- | --- |
+| `user_pleasure` | 用户愉悦度 | [-1.0, 1.0] |
+| `user_arousal` | 用户激活度 | [-1.0, 1.0] |
+| `user_dominance` | 用户支配度 | [-1.0, 1.0] |
+| `vitality` | AI 角色生理活力值 | [0.0, 100.0] |
+| `current_pleasure` | AI 当前愉悦度 | [-1.0, 1.0] |
+| `current_arousal` | AI 当前激活度 | [-1.0, 1.0] |
+| `current_dominance` | AI 当前支配度 | [-1.0, 1.0] |
+### 输出预测 (3维)
+| 标签名 | 说明 | 范围 |
+| --- | --- | --- |
+| `delta_pleasure` | 愉悦度变化量 | 理论无限制，通常 [-1, 1] |
+| `delta_arousal` | 激活度变化量 | 理论无限制，通常 [-1, 1] |
+| `delta_dominance` | 支配度变化量 | 理论无限制，通常 [-1, 1] |
+> **注**：压力变化量 ($\Delta Pressure$) 不由模型直接预测，而是根据 PAD 变化通过动力学公式动态计算：
+> $$\Delta Pressure = 1.0 \times (-\Delta P) + 0.8 \times (\Delta A) + 0.6 \times (-\Delta D)$$
+## 🎻 项目愿景与定位
+Chordia（弦音）是一个基于 **PAD 情绪演化模型** 的 AI 动力学内核。它旨在打破传统 AI "静态人设"的僵局，通过快速预测情绪状态转移，让 AI 角色具备真实的"情感惯性"和动态情绪响应能力。
+### 核心技术：情绪状态转移预测
+Chordia 通过深度学习模型，在 **< 1ms** 内完成情绪状态转移的预测，为虚拟角色提供实时的情绪演化指导。
+#### 工作原理
+1. **输入维度**：捕捉当前交互的完整情绪状态
+   - **用户情绪状态** (User PAD): 用户当前的情绪极性（愉悦度/激活度/支配度）
+   - **AI 生理指标** (Vitality): 角色的体力/活力值
+   - **AI 当前情绪** (Current PAD): 角色当前���基准情绪状态
+2. **输出预测**：计算情绪状态转移量
+   - **ΔPAD** (Delta PAD): 预测下一时刻的情绪偏移量
+   - 通过 `New_PAD = Current_PAD + ΔPAD` 实时更新角色状态
+3. **训练数据来源**：
+   - **当前版本**：基于 AI 合成数据训练，模拟多样化的交互场景和情绪转移模式
+   - **个性化训练**：开发者可以使用自己的对话历史，通过 PAD 标注后训练专属的 Chordia 模型，实现"千人千面"的个性化情绪响应
+#### 应用场景
+* **角色扮演优化**：让虚拟角色的情绪反应更贴合人设，避免 OOC（Out of Character）
+* **情感一致性维护**：避免情绪突变，保持"情感惯性"和连贯性
+* **动态人格调整**：根据交互历史自适应调整情绪敏感度
+* **实时情绪引导**：为对话系统提供即时的情绪表达建议
+* **个性化情感模型**：基于用户数据训练专属 Chordia，打造独一无二的 AI 人格
+## ⚖️ 开源协议与道德守则
+本项目采用 **CreativeML Open RAIL-M** 协议发布。该协议赋予你使用、修改和商业化的自由，但你必须遵守以下行为约束：
+### 🚫 禁止行为 (Use Restrictions)
+* **严禁用于心理医疗建议**：Chordia 模拟的情绪反馈**不具备**医学有效性。严禁将其作为心理健康诊断、精神疾病治疗或自杀干预工具。它是一个文学与娱乐性质的情感内核。
+* **禁止情感操纵**：禁止利用 Chordia 模拟的脆弱或依赖情绪对未成年人或认知受限群体进行诱导、洗脑或经济榨取。
+* **透明性要求**：在任何基于 Chordia 的商业交互中，建议向用户明示其互动对象为 AI，以防止造成不必要的情感误导。
+### ⚠️ 风险提示
+开发者需知晓，由于 Chordia 具备极强的情感诱导能力（如在测试中表现出的泣不成声或极度失落反应），在部署时应建立**安全熔断机制**。当 PAD 数值触发极端阈值时，建议中断人设模拟并提供专业援助引导。
+## 🤝 协作与致谢 (Credits)
+本项目由 **Corolin** 主导开发，并由多位人工智能助手协同完成：
+*   **设计协作 (Design)**: [DeepSeek](https://www.deepseek.com/), [Google Gemini](https://gemini.google.com/) —— 协助进行架构设计、数学模型推演及心理学公式验证。
+*   **开发协作 (Development)**: [Claude Code](https://claude.ai/), [GLM 4.7](https://chatglm.cn/), [Google Gemini](https://gemini.google.com/) —— 协作编写核心逻辑、优化训练流程及重构代码规范。
+---
+拨动心智的弦，解析共鸣的瞬感。

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@@ -1,161 +0,0 @@
----
-license: creativeml-openrail-m
-library_name: pytorch
-tags:
-- roleplay
-- emotional-intelligence
-- pad-model
-- character-logic
-- emotional-dynamics
-- conversational-ai
-- agents
-- empathy
-- personality-simulation
-- chinese
-- fine-tuned
-metrics:
-- mae
-- r2
-pipeline_tag: tabular-classification
----
-# Chordia: High-Precision AI Emotional Dynamics Core
-> **Plucking the strings of the mind, analyzing the instantaneous sense of resonance.**
-A deep learning-based AI emotional evolution prediction system. This project utilizes a Multi-Layer Perceptron (MLP) to fit emotional state transitions during interactions, providing AI characters with sub-millisecond physiological and emotional response capabilities.
-## 🎯 Core Architecture: Decoupling Perception and Logic
-This project adopts a dual-architecture of "**Core Perception Prediction + Dynamic Logic Mapping**":
-*   **Perception Kernel (MLP)**: Focuses on predicting the trend of core emotional polarity (PAD) transitions.
-*   **Runtime Mapping (Engine)**: Derives pressure values through linear scaling and physical formulas, achieving dynamic personality adjustment.
-## 📦 Version Information
-**Current Version**: `v0.0.1-alpha` (Chordia-P100)
-This version consists of the optimal weights extracted from our training machine, fully verified and tested for reproducibility, offering the best stability and prediction accuracy.
-### Training Environment
-The model was trained in the following hardware environment:
-| Component | Specification |
-| --- | --- |
-| **GPU** | NVIDIA Tesla P100-PCIE-16GB (16GB HBM2) |
-| **CUDA Version** | 12.8 |
-| **Driver Version** | 570.169 |
-| **Compute Capability** | 6.0 (Pascal Architecture) |
-### Reproducibility Guarantee
-- ✅ **Code Reproducibility**: 100% - All training code is open-sourced.
-- ✅ **Configuration Reproducibility**: 100% - Training configuration files are identical.
-- ✅ **Weight Consistency**: Identical to the version on the training machine.
-- ✅ **Performance Verification**: Achieves the same metrics on the standard test set.
-- 📄 **Training Logs**:
-   - `chordia_v0.0.1-alpha_training.log` - Training summary (1.7KB)
-   - `chordia_v0.0.1-alpha_training_full.log` - Full training record (604KB)
-## 🚀 Key Performance Indicators (Benchmark)
-After 500-600 epochs of training, the model demonstrates strong fitting capabilities:
-| Dimension | $R^2$ (Explained Variance) | MAE (Mean Absolute Error) | Psychological Significance |
-| --- | --- | --- | --- |
-| **ΔP (Pleasure)** | **0.488** | **0.123** | **Empathy**: Accurately perceives likes and dislikes from environmental stimuli. |
-| **ΔA (Arousal)** | **0.550** | **0.112** | **Expressiveness**: Precisely predicts emotional tension and reaction intensity. |
-| **ΔD (Dominance)** | **0.058** | **0.097** | **Consistency**: Maintains personality background, ensuring dominance stability. |
-> **💡 Design Philosophy**: The low $R^2$ for $\Delta D$ is intended to ensure the long-term stability of the AI's dominance, avoiding unnatural fluctuations in personality traits due to random inputs.
-| Metric | Value | Description |
-| --- | --- | --- |
-| **Test MAE** | **0.111** | Overall prediction error |
-| **Test $R^2$ (Mean)** | **0.366** | Average explained variance |
-| **Test $R^2$ (Robust)** | **0.447** | Robust explained variance |
-| **Validation Loss** | **0.023** | Best validation set loss |
-| **Inference Latency** | **< 1ms** | Single inference time |
-*   **Training Stability**: Uses AdamW optimizer (lr=0.0005) combined with Cosine Annealing learning rate scheduling (T_max=600), and an early stopping mechanism (patience=150) to prevent overfitting.
-## 📊 Input/Output Specifications
-### Input Features (7 Dimensions)
-| Feature Name | Description | Range |
-| --- | --- | --- |
-| `user_pleasure` | User Pleasure | [-1.0, 1.0] |
-| `user_arousal` | User Arousal | [-1.0, 1.0] |
-| `user_dominance` | User Dominance | [-1.0, 1.0] |
-| `vitality` | AI Character Physiological Vitality | [0.0, 100.0] |
-| `current_pleasure` | AI Current Pleasure | [-1.0, 1.0] |
-| `current_arousal` | AI Current Arousal | [-1.0, 1.0] |
-| `current_dominance` | AI Current Dominance | [-1.0, 1.0] |
-### Output Predictions (3 Dimensions)
-| Label Name | Description | Range |
-| --- | --- | --- |
-| `delta_pleasure` | Change in Pleasure | Theoretically unlimited, usually [-1, 1] |
-| `delta_arousal` | Change in Arousal | Theoretically unlimited, usually [-1, 1] |
-| `delta_dominance` | Change in Dominance | Theoretically unlimited, usually [-1, 1] |
-> **Note**: Pressure change ($\Delta Pressure$) is not directly predicted by the model but is dynamically calculated from PAD changes via a kinetic formula:
-> $$\Delta Pressure = 1.0 	imes (-\Delta P) + 0.8 	imes (\Delta A) + 0.6 	imes (-\Delta D)$$
-## 🎻 Project Vision and Positioning
-Chordia is an AI dynamics core based on the **PAD Emotional Evolution Model**. It aims to break the stalemate of "static personas" in traditional AI by rapidly predicting emotional state transitions, giving AI characters real "emotional inertia" and dynamic emotional response capabilities.
-### Core Technology: Emotional State Transition Prediction
-Chordia completes the prediction of emotional state transitions in **< 1ms**, providing real-time emotional evolution guidance for virtual characters.
-#### How it Works
-1.  **Input Dimensions**: Captures the complete emotional state of the current interaction.
-    -   **User Emotional State** (User PAD): The user's current emotional polarity.
-    -   **AI Physiological Metrics** (Vitality): The character's stamina/vitality.
-    -   **AI Current Emotion** (Current PAD): The character's current baseline emotional state.
-2.  **Output Prediction**: Calculates the amount of emotional state transition.
-    -   **ΔPAD** (Delta PAD): Predicts the emotional offset for the next moment.
-    -   Update character state in real-time via `New_PAD = Current_PAD + ΔPAD`.
-3.  **Data Sources**:
-    -   **Current Version**: Trained on AI-synthesized data, simulating diverse interaction scenarios and emotional transition patterns.
-    -   **Personalized Training**: Developers can use their own conversation history, labeled with PAD, to train a dedicated Chordia model for unique emotional responses.
-#### Application Scenarios
-*   **Roleplay Optimization**: Makes virtual characters' emotional reactions more consistent with their persona, avoiding OOC (Out of Character) moments.
-*   **Emotional Consistency Maintenance**: Avoids sudden emotional shifts, maintaining "emotional inertia" and continuity.
-*   **Dynamic Personality Adjustment**: Adaptively adjusts emotional sensitivity based on interaction history.
-*   **Real-time Emotional Guidance**: Provides instant emotional expression suggestions for dialogue systems.
-*   **Personalized Emotional Models**: Build unique AI personalities based on user data.
-## ⚖️ License and Ethics Code
-This project is released under the **CreativeML Open RAIL-M** license. This license grants you the freedom to use, modify, and commercialize the project, provided you adhere to the following behavioral constraints:
-### 🚫 Prohibited Behaviors (Use Restrictions)
-*   **Medical Advice Prohibited**: The emotional feedback simulated by Chordia is **not** medically valid. It is strictly forbidden to use it as a tool for mental health diagnosis, psychiatric treatment, or suicide intervention. It is an emotional core for literary and entertainment purposes.
-*   **Emotional Manipulation Prohibited**: Using Chordia to simulate vulnerable or dependent emotions to induce, brainwash, or economically exploit minors or cognitively limited groups is prohibited.
-*   **Transparency Requirement**: In any commercial interaction based on Chordia, it is recommended to clearly state to users that they are interacting with an AI to prevent unnecessary emotional misunderstanding.
-### ⚠️ Risk Warning
-Developers should be aware that because Chordia possesses strong emotional induction capabilities (e.g., reactions of uncontrollable sobbing or extreme dejection shown in tests), a **safety cutoff mechanism** should be established during deployment. When PAD values trigger extreme thresholds, it is recommended to interrupt the persona simulation and provide professional assistance guidance.
-## 🤝 Credits and Acknowledgements
-This project is led by **Corolin** and completed in collaboration with several AI assistants:
-*   **Design**: [DeepSeek](https://www.deepseek.com/), [Google Gemini](https://gemini.google.com/) — Assisted with architectural design, mathematical model derivation, and psychological formula verification.
-*   **Development**: [Claude Code](https://claude.ai/), [GLM 4.7](https://chatglm.cn/), [Google Gemini](https://gemini.google.com/) — Collaborated on core logic, training process optimization, and code standard refactoring.
----
-*Note: This document was translated by Google Gemini.*