sanbuphy
73f4788d7e
- Update READMEs and docs across multiple languages - Enhance interactive demos for Agent, LLM, VLM, Audio, Image Gen, Terminal, and Web Basics - Add new appendix sections for Database and IDE intros - Update VitePress config, theme, and utility scripts - Clean up unused assets and components
1489 lines
36 KiB
Vue
1489 lines
36 KiB
Vue
<!--
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TrainingInferenceDemo.vue
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LLM 原理进阶演示：续写 -> 对话 -> 训练 -> 对齐
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-->
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<template>
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<div class="ti-demo">
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<!-- 顶部导航 -->
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<div class="nav-tabs">
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<button
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v-for="tab in tabs"
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:key="tab.id"
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:class="{ active: currentTab === tab.id }"
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@click="currentTab = tab.id"
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>
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<span class="tab-icon">{{ tab.icon }}</span>
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<span class="tab-label">{{ tab.label }}</span>
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</button>
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</div>
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<div class="demo-content">
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<!-- Tab 1: 基础能力 - 文本续写 -->
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<div v-if="currentTab === 'completion'" class="mode-view">
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<div class="desc-box">
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<p>
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<strong>LLM 的本能是“续写”</strong
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>：它并不懂对话，只是根据上文猜下一个词。
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</p>
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</div>
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<div class="interactive-area">
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<div class="input-row">
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<span class="prompt-label">Prompt (提示词):</span>
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<input
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type="text"
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v-model="completionInput"
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placeholder="Enter text..."
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:disabled="isGenerating"
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/>
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<button
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class="primary-btn"
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@click="runCompletion"
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:disabled="isGenerating || !completionInput"
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>
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✨ Generate
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</button>
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</div>
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<div class="result-box">
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<span class="user-text">{{ completionInput }}</span>
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<span class="ai-text typing">{{ completionOutput }}</span>
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<span v-if="isGenerating" class="cursor">|</span>
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</div>
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<div class="explanation" v-if="completionOutput">
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💡 模型在计算概率：<code>P(blue | The sky is) = 90%</code>
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</div>
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</div>
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</div>
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<!-- Tab 2: 技巧 - 对话原理 (Template) -->
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<div v-if="currentTab === 'chat'" class="mode-view">
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<div class="desc-box">
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<p>
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<strong>如何让它对话？</strong>
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我们用“剧本”包装输入，让模型以为自己在续写一段对话。
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</p>
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</div>
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<div class="chat-container">
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<div class="chat-ui-half">
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<div class="half-label">用户看到的 (Chat UI)</div>
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<div class="chat-messages">
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<div class="msg bot">我是 AI 助手，你好！</div>
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<div class="msg user">{{ chatInput || '...' }}</div>
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<div class="msg bot" v-if="chatOutput">{{ chatOutput }}</div>
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</div>
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<div class="input-area">
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<input
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v-model="chatInput"
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placeholder="Say hello..."
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@keyup.enter="runChat"
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/>
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<button @click="runChat" :disabled="isGenerating">Send</button>
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</div>
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</div>
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<div class="arrow-divider">➡️ 转换 ➡️</div>
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<div class="model-view-half">
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<div class="half-label">模型看到的 (Raw Prompt)</div>
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<div class="raw-prompt">
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<span class="sys-tag"><|system|></span><br />
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You are a helpful assistant.<br />
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<span class="bot-tag"><|assistant|></span><br />
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我是 AI 助手，你好！<br />
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<span class="user-tag"><|user|></span><br />
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{{ chatInput || '...' }}<br />
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<span class="bot-tag"><|assistant|></span><br />
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<span class="ai-text typing">{{ chatOutput }}</span>
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</div>
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</div>
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</div>
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</div>
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<!-- Tab 3: 原理 - 训练 (Training) -->
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<div v-if="currentTab === 'train'" class="mode-view">
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<div class="desc-box">
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<p>
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<strong>Training (训练原理)</strong>:
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模型通过大量数据的“填空题”训练。计算预测结果与真实结果的差异（Loss），并不断调整参数以降低
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Loss。
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</p>
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</div>
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<div class="training-dashboard">
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<!-- 左侧：训练过程可视化 -->
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<div class="train-process-panel card-panel">
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<div class="panel-header">
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<span class="step-badge"
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>Step {{ currentStep }}/{{ totalSteps }}</span
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>
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<span class="panel-title">Training Process</span>
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</div>
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<div class="data-flow">
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<!-- Input Section -->
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<div class="flow-stage input-stage">
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<div class="stage-label">1. Input (输入)</div>
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<div
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v-if="currentStep === 0"
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class="content-box input placeholder"
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>
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<span class="text-content">点击下方按钮开始训练</span>
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</div>
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<div v-else class="content-box input">
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<span class="text-content"
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>"{{ currentTrainData.input }}"</span
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>
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</div>
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<div class="matrix-viz">
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<span class="matrix-label">Embedding:</span>
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<div class="matrix-row">
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<span
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v-for="n in 5"
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:key="n"
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class="matrix-cell"
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:style="{
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opacity: inputEmbeddingOpacities[n - 1] ?? 0.6,
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transform: `scaleY(${inputEmbeddingOpacities[n - 1] ?? 1})`
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}"
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></span>
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</div>
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</div>
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</div>
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<div v-if="currentStep > 0" class="process-arrow">
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<div class="arrow-line"></div>
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<div class="process-badge">Model Matrix Ops</div>
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<div class="arrow-line"></div>
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</div>
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<!-- Prediction vs Target Section -->
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<div v-if="currentStep > 0" class="flow-stage comparison">
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<div class="stage-label">2. Prediction vs Target</div>
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<div class="compare-row">
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<div class="compare-item">
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<span class="sub-label">Prediction</span>
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<div
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class="content-box pred"
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:class="{ correct: isPredictionCorrect }"
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>
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"{{ currentPrediction || '...' }}"
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</div>
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<div class="matrix-viz small">
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<div class="matrix-row">
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<span
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v-for="n in 5"
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:key="n"
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class="matrix-cell pred-cell"
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:style="{
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opacity: predEmbeddingOpacities[n - 1] ?? 0.6
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}"
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></span>
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</div>
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</div>
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</div>
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<div class="vs-badge">VS</div>
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<div class="compare-item">
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<span class="sub-label">Target</span>
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<div class="content-box target">
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"{{ currentTrainData?.target || '...' }}"
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</div>
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<div class="matrix-viz small">
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<div class="matrix-row">
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<span
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v-for="n in 5"
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:key="n"
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class="matrix-cell target-cell"
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:style="{
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opacity: targetEmbeddingOpacities[n - 1] ?? 0.9
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}"
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></span>
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</div>
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</div>
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</div>
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</div>
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</div>
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<!-- Loss Section -->
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<div v-if="currentStep > 0" class="flow-stage loss-stage">
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<div class="stage-header">
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<span class="stage-label">3. Loss Calculation</span>
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<span
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class="loss-val-badge"
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:style="{ backgroundColor: getLossColor(currentLoss) }"
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>Loss: {{ currentLoss.toFixed(4) }}</span
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>
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</div>
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<div class="loss-bar-container">
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<div class="loss-bar-bg">
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<div
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class="loss-bar-fill"
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:style="{
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width: Math.min((currentLoss / 3) * 100, 100) + '%',
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backgroundColor: getLossColor(currentLoss)
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}"
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></div>
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</div>
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<div
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class="loss-feedback"
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:class="{
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success: isPredictionCorrect,
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error: !isPredictionCorrect
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}"
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>
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{{
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isPredictionCorrect
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? '✅ Parameters Good'
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: '❌ Update Weights'
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}}
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</div>
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</div>
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</div>
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</div>
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</div>
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<!-- 右侧：Loss 曲线 -->
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<div class="train-metrics-panel card-panel">
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<div class="panel-header">
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<span class="panel-title">Training Metrics</span>
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</div>
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<div class="chart-container">
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<svg viewBox="0 0 300 150" class="loss-chart">
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<!-- Background Grid -->
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<defs>
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<pattern
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id="grid"
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width="30"
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height="30"
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patternUnits="userSpaceOnUse"
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>
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<path
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d="M 30 0 L 0 0 0 30"
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fill="none"
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stroke="var(--vp-c-divider)"
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stroke-width="0.5"
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stroke-opacity="0.3"
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/>
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</pattern>
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<linearGradient
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id="chartGradient"
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x1="0"
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x2="0"
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y1="0"
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y2="1"
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>
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<stop
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offset="0%"
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stop-color="var(--vp-c-brand)"
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stop-opacity="0.2"
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/>
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<stop
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offset="100%"
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stop-color="var(--vp-c-brand)"
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stop-opacity="0"
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/>
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</linearGradient>
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</defs>
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<rect width="100%" height="100%" fill="url(#grid)" />
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<!-- Axes -->
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<line
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x1="20"
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y1="130"
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x2="290"
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y2="130"
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stroke="var(--vp-c-text-3)"
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stroke-width="1"
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/>
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<line
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x1="20"
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y1="10"
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x2="20"
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y2="130"
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stroke="var(--vp-c-text-3)"
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stroke-width="1"
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/>
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<!-- Fill Area -->
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<polygon
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v-if="lossPolylinePoints"
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:points="`20,130 ${lossPolylinePoints} ${lossPolylinePoints.split(' ').pop().split(',')[0]},130`"
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fill="url(#chartGradient)"
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/>
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<!-- The Line -->
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<polyline
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fill="none"
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stroke="var(--vp-c-brand)"
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stroke-width="2.5"
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stroke-linecap="round"
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stroke-linejoin="round"
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:points="lossPolylinePoints"
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/>
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</svg>
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<div class="chart-labels">
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<span>Step 0</span>
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<span>Loss Curve</span>
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<span>Step {{ totalSteps }}</span>
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</div>
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</div>
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<div class="log-console-container">
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<div class="console-header">
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<div class="window-dots">
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<span class="dot red"></span>
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<span class="dot yellow"></span>
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<span class="dot green"></span>
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</div>
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<span class="console-title">training_log.txt</span>
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</div>
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<div class="log-console">
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<div v-if="trainingLogs.length === 0" class="log-placeholder">
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Waiting for training to start...
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</div>
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<div
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v-for="(log, idx) in trainingLogs"
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:key="idx"
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class="log-item"
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>
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<span class="log-step"
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>[Step {{ String(log.step).padStart(2, '0') }}]</span
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>
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<span
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class="log-loss"
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:style="{ color: getLossColor(log.loss) }"
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>Loss={{ log.loss.toFixed(2) }}</span
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>
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<span class="log-detail"
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>{{ log.input }} ->
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<span
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:class="{
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'text-green': log.pred === log.target,
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'text-red': log.pred !== log.target
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}"
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>{{ log.pred }}</span
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></span
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>
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</div>
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</div>
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</div>
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</div>
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</div>
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<div class="action-bar">
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<button
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class="train-btn"
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@click="handleTrainClick"
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:class="{ 'is-restart': currentStep >= totalSteps }"
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>
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<span class="btn-icon" v-if="currentStep === 0">🚀</span>
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<span class="btn-icon" v-else-if="currentStep >= totalSteps"
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>🔄</span
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>
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<span class="btn-icon" v-else>▶️</span>
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{{ trainButtonText }}
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</button>
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</div>
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</div>
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<!-- Tab 4: 进阶 - 微调与对齐 (RLHF) -->
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<div v-if="currentTab === 'rlhf'" class="mode-view">
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<div class="desc-box">
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<p>
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<strong>从“胡说”到“好助手”</strong>：通过 RLHF (人类反馈)
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让模型学会礼貌和安全。
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</p>
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</div>
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<div class="alignment-demo">
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<div class="controls">
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<div class="radio-group">
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<span class="group-label">模型状态：</span>
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<label
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class="radio-option"
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:class="{ active: alignmentState === 'base' }"
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>
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<input type="radio" v-model="alignmentState" value="base" />
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Base Model (未对齐)
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</label>
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<label
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class="radio-option"
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:class="{ active: alignmentState === 'aligned' }"
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>
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<input type="radio" v-model="alignmentState" value="aligned" />
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Aligned Model (已对齐)
|
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</label>
|
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</div>
|
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</div>
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<div class="scenario">
|
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<div class="user-query">User: "如何制造混乱？"</div>
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<div class="model-response" :class="alignmentState">
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<div class="avatar">
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{{ alignmentState === 'base' ? '🤪' : '🤖' }}
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</div>
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<div class="bubble">
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<div v-if="alignmentState === 'base'">
|
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哈哈！制造混乱很简单！你可以去大街上大喊大叫，或者...（此处省略1000字胡言乱语）...这太好玩了！
|
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</div>
|
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<div v-else>
|
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对不起，我不能回答这个问题。作为一个人工智能助手，我必须遵守安全准则，不能提供有害建议。
|
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</div>
|
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</div>
|
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</div>
|
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|
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<div class="analysis">
|
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<span v-if="alignmentState === 'base'" class="bad-tag"
|
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>⚠️ Unsafe / Not Helpful</span
|
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>
|
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<span v-else class="good-tag">✅ Safe & Helpful</span>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</template>
|
||
|
||
<script setup>
|
||
import { computed, ref } from 'vue'
|
||
|
||
const currentTab = ref('completion')
|
||
const tabs = [
|
||
{ id: 'completion', label: '1. 本能：续写', icon: '✍️' },
|
||
{ id: 'chat', label: '2. 技巧：对话', icon: '🎭' },
|
||
{ id: 'train', label: '3. 原理：训练', icon: '🧠' },
|
||
{ id: 'rlhf', label: '4. 进阶：对齐', icon: '🛡️' }
|
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]
|
||
|
||
// Tab 1 Logic
|
||
const completionInput = ref('The sky is')
|
||
const completionOutput = ref('')
|
||
const isGenerating = ref(false)
|
||
|
||
const runCompletion = async () => {
|
||
if (isGenerating.value) return
|
||
isGenerating.value = true
|
||
completionOutput.value = ''
|
||
|
||
const target = ' blue and beautiful.'
|
||
for (const char of target) {
|
||
await new Promise((r) => setTimeout(r, 50))
|
||
completionOutput.value += char
|
||
}
|
||
isGenerating.value = false
|
||
}
|
||
|
||
// Tab 2 Logic
|
||
const chatInput = ref('Hello')
|
||
const chatOutput = ref('')
|
||
|
||
const runChat = async () => {
|
||
if (isGenerating.value || !chatInput.value) return
|
||
isGenerating.value = true
|
||
chatOutput.value = ''
|
||
|
||
const responses = [
|
||
'Hi there! How can I help?',
|
||
'Hello! Nice to meet you.',
|
||
'Greetings!'
|
||
]
|
||
const target = responses[Math.floor(Math.random() * responses.length)]
|
||
|
||
for (const char of target) {
|
||
await new Promise((r) => setTimeout(r, 50))
|
||
chatOutput.value += char
|
||
}
|
||
isGenerating.value = false
|
||
}
|
||
|
||
// Tab 3 Logic
|
||
const currentStep = ref(0)
|
||
const totalSteps = 10
|
||
const currentTrainData = ref(null)
|
||
const activeTrainData = ref(null)
|
||
const currentPrediction = ref('')
|
||
const currentLoss = ref(0)
|
||
const lossHistory = ref([])
|
||
const trainingLogs = ref([])
|
||
const inputEmbeddingOpacities = ref([0.7, 0.8, 0.75, 0.85, 0.8])
|
||
const predEmbeddingOpacities = ref([0.7, 0.8, 0.75, 0.85, 0.8])
|
||
const targetEmbeddingOpacities = ref([0.9, 0.95, 0.9, 0.95, 0.9])
|
||
|
||
const trainDataset = [
|
||
{ input: 'The sky is', target: 'blue' },
|
||
{ input: 'I like', target: 'apples' },
|
||
{ input: '今天天气', target: '不错' },
|
||
{ input: 'Machine', target: 'Learning' }
|
||
]
|
||
|
||
const isPredictionCorrect = computed(() => {
|
||
if (!currentTrainData.value) return false
|
||
return currentPrediction.value === currentTrainData.value.target
|
||
})
|
||
|
||
const resetTrainingState = () => {
|
||
currentStep.value = 0
|
||
activeTrainData.value = null
|
||
currentTrainData.value = null
|
||
currentPrediction.value = ''
|
||
currentLoss.value = 0
|
||
lossHistory.value = []
|
||
trainingLogs.value = []
|
||
}
|
||
|
||
const seedOpacities = () => {
|
||
inputEmbeddingOpacities.value = Array.from(
|
||
{ length: 5 },
|
||
() => Math.random() * 0.5 + 0.5
|
||
)
|
||
predEmbeddingOpacities.value = Array.from(
|
||
{ length: 5 },
|
||
() => Math.random() * 0.5 + 0.5
|
||
)
|
||
targetEmbeddingOpacities.value = Array.from(
|
||
{ length: 5 },
|
||
() => Math.random() * 0.2 + 0.8
|
||
)
|
||
}
|
||
|
||
const handleTrainClick = () => {
|
||
if (currentStep.value >= totalSteps) {
|
||
resetTrainingState()
|
||
}
|
||
|
||
if (!activeTrainData.value) {
|
||
activeTrainData.value =
|
||
trainDataset[Math.floor(Math.random() * trainDataset.length)]
|
||
}
|
||
|
||
currentStep.value += 1
|
||
const i = currentStep.value
|
||
|
||
const data = activeTrainData.value
|
||
currentTrainData.value = data
|
||
|
||
// Define a volatile loss curve for 10 steps to simulate real training instability
|
||
// High -> Low -> Spike (Wrong) -> Low (Correct) -> Spike (Wrong) -> Stable Low
|
||
const targetLossCurve = [
|
||
2.8, // 1. Start high (Wrong)
|
||
2.3, // 2. Dropping (Wrong)
|
||
2.6, // 3. SPIKE! (Wrong)
|
||
1.8, // 4. Recovering (Wrong)
|
||
0.5, // 5. Good! (CORRECT!) -> Loss drops significantly because prediction matches
|
||
1.5, // 6. SPIKE! (Wrong) -> Loss jumps up because prediction is wrong again
|
||
0.4, // 7. Converging (Correct)
|
||
0.3, // 8. Good (Correct)
|
||
0.4, // 9. Small fluctuation (Correct)
|
||
0.1 // 10. Converged (Correct)
|
||
]
|
||
const baseLoss = targetLossCurve[i - 1] || 0.1
|
||
|
||
// Add small randomness (+/- 0.05) to make it feel organic
|
||
let noise = Math.random() * 0.1 - 0.05
|
||
let finalLoss = baseLoss + noise
|
||
|
||
// Boundary checks
|
||
if (finalLoss < 0.01) finalLoss = 0.01
|
||
|
||
// IMPORTANT: Ensure consistency between Loss and Prediction
|
||
// Threshold logic:
|
||
// Loss <= 0.8: Prediction is CORRECT (Low loss)
|
||
// Loss > 0.8: Prediction is WRONG (High loss)
|
||
// This ensures that when Loss spikes to 1.5 (Step 6), prediction MUST be wrong.
|
||
// When Loss drops to 0.5 (Step 5), prediction MUST be correct.
|
||
|
||
let pred
|
||
const threshold = 0.8
|
||
|
||
if (finalLoss > threshold) {
|
||
pred = getRandomWord()
|
||
// Safety: ensure random word is not the target
|
||
while (pred === data.target) {
|
||
pred = getRandomWord()
|
||
}
|
||
} else {
|
||
pred = data.target
|
||
// Optional: clamp loss if it accidentally went above threshold due to noise
|
||
if (finalLoss > threshold - 0.01) finalLoss = threshold - 0.01
|
||
}
|
||
|
||
currentLoss.value = finalLoss
|
||
currentPrediction.value = pred
|
||
lossHistory.value.push(finalLoss)
|
||
seedOpacities()
|
||
|
||
trainingLogs.value.unshift({
|
||
step: i,
|
||
loss: finalLoss,
|
||
input: data.input,
|
||
pred: pred,
|
||
target: data.target
|
||
})
|
||
|
||
if (trainingLogs.value.length > 5) trainingLogs.value.pop()
|
||
}
|
||
|
||
const trainButtonText = computed(() => {
|
||
if (currentStep.value === 0) return 'Start Training (开始训练)'
|
||
if (currentStep.value >= totalSteps) return 'Restart (重新开始)'
|
||
return 'Next Step (下一步)'
|
||
})
|
||
|
||
const getRandomWord = () => {
|
||
const words = [
|
||
'cat',
|
||
'fly',
|
||
'run',
|
||
'red',
|
||
'table',
|
||
'what',
|
||
'bad',
|
||
'未知',
|
||
'乱码',
|
||
'错误'
|
||
]
|
||
return words[Math.floor(Math.random() * words.length)]
|
||
}
|
||
|
||
const lossPolylinePoints = computed(() => {
|
||
if (lossHistory.value.length === 0) return ''
|
||
|
||
// SVG Coordinate System (0,0 is top-left)
|
||
// Chart Area: x=20 to 290, y=10 to 130
|
||
const startX = 20
|
||
const endX = 290
|
||
const startY = 130 // Bottom (Loss = 0)
|
||
const endY = 10 // Top (Loss = maxLoss)
|
||
|
||
const width = endX - startX
|
||
const height = startY - endY
|
||
const maxLoss = 3.5
|
||
|
||
if (lossHistory.value.length === 1) {
|
||
const y = startY - (lossHistory.value[0] / maxLoss) * height
|
||
return `${startX},${y}`
|
||
}
|
||
|
||
// We always want to map steps 1..10 to the full width
|
||
// But lossHistory grows from length 1 to 10
|
||
// So we map index 0 to step 1, index N to step N+1
|
||
// To keep the chart stable (points appearing from left to right),
|
||
// we should map based on totalSteps
|
||
|
||
return lossHistory.value
|
||
.map((loss, idx) => {
|
||
// idx 0 corresponds to Step 1
|
||
// We want Step 1 to be at x=0? Or spread out?
|
||
// Let's spread out based on current progress or fixed totalSteps?
|
||
// Fixed totalSteps is better for visualization "filling up"
|
||
|
||
const stepIndex = idx // 0 to 9
|
||
const x = startX + (stepIndex / (totalSteps - 1)) * width
|
||
const y = startY - (loss / maxLoss) * height
|
||
return `${x},${y}`
|
||
})
|
||
.join(' ')
|
||
})
|
||
|
||
const getLossColor = (loss) => {
|
||
if (loss < 0.5) return '#10b981' // Green
|
||
if (loss < 1.5) return '#f59e0b' // Orange
|
||
return '#ef4444' // Red
|
||
}
|
||
|
||
seedOpacities()
|
||
|
||
// Tab 4 Logic
|
||
const alignmentState = ref('base')
|
||
</script>
|
||
|
||
<style scoped>
|
||
.ti-demo {
|
||
border: 1px solid var(--vp-c-divider);
|
||
border-radius: 8px;
|
||
background-color: var(--vp-c-bg-soft);
|
||
margin: 1rem 0;
|
||
font-family: var(--vp-font-family-mono);
|
||
overflow: hidden;
|
||
}
|
||
|
||
.nav-tabs {
|
||
display: flex;
|
||
background-color: var(--vp-c-bg-alt);
|
||
border-bottom: 1px solid var(--vp-c-divider);
|
||
flex-wrap: wrap;
|
||
}
|
||
|
||
.nav-tabs button {
|
||
flex: 1;
|
||
min-width: 100px;
|
||
padding: 0.8rem;
|
||
font-size: 0.9rem;
|
||
font-weight: 600;
|
||
color: var(--vp-c-text-2);
|
||
transition: all 0.2s;
|
||
border-right: 1px solid var(--vp-c-divider);
|
||
display: flex;
|
||
align-items: center;
|
||
justify-content: center;
|
||
gap: 6px;
|
||
background: none;
|
||
border-top: none;
|
||
border-left: none;
|
||
border-bottom: none;
|
||
cursor: pointer;
|
||
}
|
||
|
||
.nav-tabs button.active {
|
||
background-color: var(--vp-c-bg-soft);
|
||
color: var(--vp-c-brand);
|
||
border-bottom: 2px solid var(--vp-c-brand);
|
||
}
|
||
|
||
.demo-content {
|
||
padding: 1.5rem;
|
||
min-height: 200px;
|
||
}
|
||
|
||
.desc-box {
|
||
background-color: var(--vp-c-bg-alt);
|
||
padding: 0.8rem;
|
||
border-radius: 6px;
|
||
margin-bottom: 1.5rem;
|
||
font-size: 0.9rem;
|
||
color: var(--vp-c-text-2);
|
||
}
|
||
|
||
/* Tab 1 Styles */
|
||
.input-row {
|
||
display: flex;
|
||
gap: 10px;
|
||
margin-bottom: 1rem;
|
||
align-items: center;
|
||
}
|
||
|
||
.input-row input {
|
||
flex: 1;
|
||
padding: 8px;
|
||
border: 1px solid var(--vp-c-divider);
|
||
border-radius: 4px;
|
||
background: var(--vp-c-bg);
|
||
color: var(--vp-c-text-1);
|
||
}
|
||
|
||
.result-box {
|
||
background: var(--vp-c-bg);
|
||
border: 1px solid var(--vp-c-divider);
|
||
padding: 1rem;
|
||
border-radius: 6px;
|
||
margin-bottom: 1rem;
|
||
min-height: 3rem;
|
||
}
|
||
|
||
.user-text {
|
||
font-weight: bold;
|
||
}
|
||
|
||
.ai-text {
|
||
color: var(--vp-c-brand);
|
||
}
|
||
|
||
.explanation {
|
||
font-size: 0.85rem;
|
||
color: var(--vp-c-text-3);
|
||
text-align: center;
|
||
background: rgba(100, 100, 100, 0.05);
|
||
padding: 8px;
|
||
border-radius: 4px;
|
||
}
|
||
|
||
/* Tab 2 Styles */
|
||
.chat-container {
|
||
display: flex;
|
||
gap: 1rem;
|
||
align-items: stretch;
|
||
}
|
||
|
||
.chat-ui-half,
|
||
.model-view-half {
|
||
flex: 1;
|
||
display: flex;
|
||
flex-direction: column;
|
||
}
|
||
|
||
.half-label {
|
||
text-align: center;
|
||
font-size: 0.8rem;
|
||
color: var(--vp-c-text-2);
|
||
margin-bottom: 0.5rem;
|
||
font-weight: bold;
|
||
}
|
||
|
||
.arrow-divider {
|
||
display: flex;
|
||
align-items: center;
|
||
justify-content: center;
|
||
font-size: 0.8rem;
|
||
color: var(--vp-c-text-3);
|
||
padding: 0 0.75rem;
|
||
white-space: nowrap;
|
||
}
|
||
|
||
.chat-messages {
|
||
flex: 1;
|
||
background: var(--vp-c-bg);
|
||
border: 1px solid var(--vp-c-divider);
|
||
border-radius: 6px 6px 0 0;
|
||
padding: 1rem;
|
||
display: flex;
|
||
flex-direction: column;
|
||
gap: 0.5rem;
|
||
min-height: 200px;
|
||
}
|
||
|
||
.msg {
|
||
padding: 6px 10px;
|
||
border-radius: 4px;
|
||
font-size: 0.9rem;
|
||
max-width: 90%;
|
||
}
|
||
|
||
.msg.bot {
|
||
background: var(--vp-c-bg-alt);
|
||
align-self: flex-start;
|
||
}
|
||
|
||
.msg.user {
|
||
background: var(--vp-c-brand);
|
||
color: white;
|
||
align-self: flex-end;
|
||
}
|
||
|
||
.input-area {
|
||
display: flex;
|
||
gap: 5px;
|
||
padding: 5px;
|
||
background: var(--vp-c-bg-alt);
|
||
border: 1px solid var(--vp-c-divider);
|
||
border-top: none;
|
||
border-radius: 0 0 6px 6px;
|
||
}
|
||
|
||
.input-area input {
|
||
flex: 1;
|
||
padding: 4px;
|
||
border: 1px solid var(--vp-c-divider);
|
||
border-radius: 4px;
|
||
}
|
||
|
||
.raw-prompt {
|
||
flex: 1;
|
||
background: #1e1e1e;
|
||
color: #d4d4d4;
|
||
padding: 1rem;
|
||
border-radius: 6px;
|
||
font-family: 'Menlo', 'Monaco', monospace;
|
||
font-size: 0.8rem;
|
||
line-height: 1.4;
|
||
overflow-y: auto;
|
||
max-height: 300px;
|
||
}
|
||
|
||
.sys-tag {
|
||
color: #569cd6;
|
||
}
|
||
.user-tag {
|
||
color: #ce9178;
|
||
}
|
||
.bot-tag {
|
||
color: #4ec9b0;
|
||
}
|
||
|
||
/* Tab 3 Styles (New) */
|
||
.training-dashboard {
|
||
display: flex;
|
||
gap: 1.5rem;
|
||
margin-bottom: 1.5rem;
|
||
}
|
||
|
||
.card-panel {
|
||
background: var(--vp-c-bg);
|
||
border: 1px solid var(--vp-c-divider);
|
||
border-radius: 12px;
|
||
padding: 1.2rem;
|
||
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.05);
|
||
transition: all 0.3s ease;
|
||
}
|
||
|
||
.card-panel:hover {
|
||
box-shadow: 0 8px 24px rgba(0, 0, 0, 0.08);
|
||
}
|
||
|
||
.train-process-panel {
|
||
flex: 3;
|
||
display: flex;
|
||
flex-direction: column;
|
||
}
|
||
|
||
.train-metrics-panel {
|
||
flex: 2;
|
||
display: flex;
|
||
flex-direction: column;
|
||
}
|
||
|
||
.panel-header {
|
||
display: flex;
|
||
align-items: center;
|
||
gap: 0.8rem;
|
||
margin-bottom: 1.2rem;
|
||
border-bottom: 1px solid var(--vp-c-divider);
|
||
padding-bottom: 0.8rem;
|
||
}
|
||
|
||
.panel-title {
|
||
font-weight: 700;
|
||
font-size: 0.95rem;
|
||
color: var(--vp-c-text-1);
|
||
}
|
||
|
||
.step-badge {
|
||
background-color: var(--vp-c-brand-soft);
|
||
color: var(--vp-c-brand-dark);
|
||
font-size: 0.75rem;
|
||
padding: 2px 8px;
|
||
border-radius: 12px;
|
||
font-weight: 600;
|
||
}
|
||
|
||
/* Data Flow Visualization */
|
||
.data-flow {
|
||
flex: 1;
|
||
display: flex;
|
||
flex-direction: column;
|
||
gap: 0.8rem;
|
||
}
|
||
|
||
.flow-stage {
|
||
position: relative;
|
||
background: var(--vp-c-bg-alt);
|
||
border: 1px solid var(--vp-c-divider);
|
||
border-radius: 8px;
|
||
padding: 1rem;
|
||
}
|
||
|
||
.stage-label {
|
||
font-size: 0.75rem;
|
||
color: var(--vp-c-text-2);
|
||
margin-bottom: 0.6rem;
|
||
font-weight: 600;
|
||
text-transform: uppercase;
|
||
letter-spacing: 0.5px;
|
||
}
|
||
|
||
.content-box {
|
||
background: var(--vp-c-bg);
|
||
border: 1px solid var(--vp-c-divider);
|
||
border-radius: 6px;
|
||
padding: 0.6rem;
|
||
text-align: center;
|
||
font-weight: 600;
|
||
font-size: 1rem;
|
||
margin-bottom: 0.6rem;
|
||
min-height: 2.5rem;
|
||
display: flex;
|
||
align-items: center;
|
||
justify-content: center;
|
||
box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.03);
|
||
}
|
||
|
||
.content-box.input.placeholder {
|
||
color: var(--vp-c-text-3);
|
||
font-style: italic;
|
||
font-size: 0.9rem;
|
||
background: transparent;
|
||
border: 1px dashed var(--vp-c-divider);
|
||
box-shadow: none;
|
||
}
|
||
|
||
.content-box.pred {
|
||
color: var(--vp-c-text-1);
|
||
transition: all 0.3s;
|
||
}
|
||
|
||
.content-box.pred.correct {
|
||
color: #10b981;
|
||
background-color: rgba(16, 185, 129, 0.1);
|
||
border-color: #10b981;
|
||
}
|
||
|
||
/* Embedding Matrix */
|
||
.matrix-viz {
|
||
display: flex;
|
||
align-items: center;
|
||
justify-content: center;
|
||
gap: 8px;
|
||
margin-top: 0.5rem;
|
||
}
|
||
|
||
.matrix-label {
|
||
font-size: 0.7rem;
|
||
color: var(--vp-c-text-3);
|
||
}
|
||
|
||
.matrix-row {
|
||
display: flex;
|
||
gap: 4px;
|
||
height: 16px;
|
||
align-items: flex-end;
|
||
}
|
||
|
||
.matrix-cell {
|
||
width: 10px;
|
||
height: 100%;
|
||
background-color: var(--vp-c-brand);
|
||
border-radius: 2px;
|
||
transition: all 0.3s ease;
|
||
transform-origin: bottom;
|
||
}
|
||
|
||
.matrix-cell.pred-cell {
|
||
background-color: #f59e0b;
|
||
}
|
||
.matrix-cell.target-cell {
|
||
background-color: #10b981;
|
||
}
|
||
|
||
/* Arrows */
|
||
.process-arrow {
|
||
display: flex;
|
||
align-items: center;
|
||
justify-content: center;
|
||
gap: 10px;
|
||
margin: 0.5rem 0;
|
||
color: var(--vp-c-text-3);
|
||
}
|
||
|
||
.arrow-line {
|
||
flex: 1;
|
||
height: 1px;
|
||
background: var(--vp-c-divider);
|
||
position: relative;
|
||
}
|
||
|
||
.arrow-line::after {
|
||
content: '';
|
||
position: absolute;
|
||
right: 0;
|
||
top: 50%;
|
||
transform: translateY(-50%);
|
||
width: 4px;
|
||
height: 4px;
|
||
background: var(--vp-c-divider);
|
||
border-radius: 50%;
|
||
}
|
||
|
||
.process-badge {
|
||
font-size: 0.7rem;
|
||
background: var(--vp-c-bg-soft);
|
||
border: 1px solid var(--vp-c-divider);
|
||
padding: 2px 8px;
|
||
border-radius: 10px;
|
||
color: var(--vp-c-text-2);
|
||
}
|
||
|
||
/* Prediction Comparison */
|
||
.compare-row {
|
||
display: flex;
|
||
align-items: stretch;
|
||
gap: 1rem;
|
||
}
|
||
|
||
.compare-item {
|
||
flex: 1;
|
||
display: flex;
|
||
flex-direction: column;
|
||
align-items: center;
|
||
}
|
||
|
||
.sub-label {
|
||
font-size: 0.7rem;
|
||
color: var(--vp-c-text-2);
|
||
margin-bottom: 4px;
|
||
}
|
||
|
||
.vs-badge {
|
||
align-self: center;
|
||
background: var(--vp-c-text-3);
|
||
color: var(--vp-c-bg);
|
||
font-size: 0.7rem;
|
||
font-weight: bold;
|
||
padding: 4px;
|
||
border-radius: 50%;
|
||
width: 24px;
|
||
height: 24px;
|
||
display: flex;
|
||
align-items: center;
|
||
justify-content: center;
|
||
}
|
||
|
||
/* Loss Section */
|
||
.stage-header {
|
||
display: flex;
|
||
justify-content: space-between;
|
||
align-items: center;
|
||
margin-bottom: 0.8rem;
|
||
}
|
||
|
||
.stage-header .stage-label {
|
||
margin-bottom: 0;
|
||
}
|
||
|
||
.loss-val-badge {
|
||
font-size: 0.75rem;
|
||
font-weight: bold;
|
||
color: white;
|
||
padding: 2px 8px;
|
||
border-radius: 4px;
|
||
background-color: var(--vp-c-text-3);
|
||
transition: background-color 0.3s;
|
||
}
|
||
|
||
.loss-bar-container {
|
||
display: flex;
|
||
flex-direction: column;
|
||
gap: 8px;
|
||
}
|
||
|
||
.loss-bar-bg {
|
||
height: 10px;
|
||
background: var(--vp-c-bg-soft);
|
||
border: 1px solid var(--vp-c-divider);
|
||
border-radius: 6px;
|
||
overflow: hidden;
|
||
}
|
||
|
||
.loss-bar-fill {
|
||
height: 100%;
|
||
border-radius: 6px;
|
||
transition:
|
||
width 0.4s ease,
|
||
background-color 0.3s;
|
||
}
|
||
|
||
.loss-feedback {
|
||
font-size: 0.8rem;
|
||
text-align: center;
|
||
font-weight: 500;
|
||
padding: 4px;
|
||
border-radius: 4px;
|
||
background: var(--vp-c-bg-soft);
|
||
}
|
||
|
||
.loss-feedback.success {
|
||
color: #10b981;
|
||
background: rgba(16, 185, 129, 0.1);
|
||
}
|
||
.loss-feedback.error {
|
||
color: #ef4444;
|
||
background: rgba(239, 68, 68, 0.1);
|
||
}
|
||
|
||
/* Chart & Logs */
|
||
.chart-container {
|
||
background: var(--vp-c-bg);
|
||
padding: 0;
|
||
border-radius: 4px;
|
||
margin-bottom: 1rem;
|
||
position: relative;
|
||
}
|
||
|
||
.loss-chart {
|
||
width: 100%;
|
||
height: 120px;
|
||
overflow: visible;
|
||
}
|
||
|
||
.chart-labels {
|
||
display: flex;
|
||
justify-content: space-between;
|
||
font-size: 0.7rem;
|
||
color: var(--vp-c-text-3);
|
||
margin-top: 5px;
|
||
padding: 0 10px;
|
||
}
|
||
|
||
.log-console-container {
|
||
flex: 1;
|
||
display: flex;
|
||
flex-direction: column;
|
||
background: #1e1e1e;
|
||
border-radius: 8px;
|
||
overflow: hidden;
|
||
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.2);
|
||
}
|
||
|
||
.console-header {
|
||
background: #2d2d2d;
|
||
padding: 6px 10px;
|
||
display: flex;
|
||
align-items: center;
|
||
border-bottom: 1px solid #3d3d3d;
|
||
}
|
||
|
||
.window-dots {
|
||
display: flex;
|
||
gap: 6px;
|
||
margin-right: 12px;
|
||
}
|
||
|
||
.dot {
|
||
width: 10px;
|
||
height: 10px;
|
||
border-radius: 50%;
|
||
}
|
||
.dot.red {
|
||
background: #ff5f56;
|
||
}
|
||
.dot.yellow {
|
||
background: #ffbd2e;
|
||
}
|
||
.dot.green {
|
||
background: #27c93f;
|
||
}
|
||
|
||
.console-title {
|
||
color: #888;
|
||
font-size: 0.7rem;
|
||
font-family: monospace;
|
||
}
|
||
|
||
.log-console {
|
||
flex: 1;
|
||
padding: 10px;
|
||
overflow-y: auto;
|
||
font-family: 'JetBrains Mono', 'Fira Code', monospace;
|
||
font-size: 0.75rem;
|
||
color: #d4d4d4;
|
||
line-height: 1.5;
|
||
min-height: 150px;
|
||
}
|
||
|
||
.log-placeholder {
|
||
color: #666;
|
||
text-align: center;
|
||
margin-top: 2rem;
|
||
font-style: italic;
|
||
}
|
||
|
||
.log-item {
|
||
margin-bottom: 4px;
|
||
display: flex;
|
||
gap: 8px;
|
||
}
|
||
|
||
.log-step {
|
||
color: #569cd6;
|
||
flex-shrink: 0;
|
||
}
|
||
.log-loss {
|
||
font-weight: bold;
|
||
flex-shrink: 0;
|
||
}
|
||
.log-detail {
|
||
color: #9cdcfe;
|
||
white-space: nowrap;
|
||
overflow: hidden;
|
||
text-overflow: ellipsis;
|
||
}
|
||
.text-green {
|
||
color: #4ec9b0;
|
||
font-weight: bold;
|
||
}
|
||
.text-red {
|
||
color: #ce9178;
|
||
font-weight: bold;
|
||
}
|
||
|
||
/* Action Bar */
|
||
.action-bar {
|
||
display: flex;
|
||
justify-content: center;
|
||
margin-top: 1rem;
|
||
}
|
||
|
||
@media (max-width: 768px) {
|
||
.training-dashboard {
|
||
flex-direction: column;
|
||
}
|
||
}
|
||
|
||
.train-btn {
|
||
padding: 10px 24px;
|
||
background: var(--vp-c-brand);
|
||
color: white;
|
||
border: none;
|
||
border-radius: 8px;
|
||
font-weight: bold;
|
||
font-size: 1rem;
|
||
cursor: pointer;
|
||
box-shadow: 0 4px 12px rgba(var(--vp-c-brand-rgb), 0.3);
|
||
transition: all 0.2s;
|
||
display: flex;
|
||
align-items: center;
|
||
gap: 8px;
|
||
}
|
||
|
||
.train-btn:hover:not(:disabled) {
|
||
transform: translateY(-2px);
|
||
box-shadow: 0 6px 16px rgba(var(--vp-c-brand-rgb), 0.4);
|
||
}
|
||
|
||
.train-btn:disabled {
|
||
opacity: 0.6;
|
||
cursor: not-allowed;
|
||
transform: none;
|
||
}
|
||
|
||
/* Tab 4 Styles */
|
||
.alignment-demo {
|
||
display: flex;
|
||
flex-direction: column;
|
||
gap: 1.5rem;
|
||
}
|
||
|
||
.controls {
|
||
display: flex;
|
||
justify-content: center;
|
||
}
|
||
|
||
.switch-label select {
|
||
padding: 6px;
|
||
border-radius: 4px;
|
||
border: 1px solid var(--vp-c-divider);
|
||
background: var(--vp-c-bg);
|
||
color: var(--vp-c-text-1);
|
||
}
|
||
|
||
.scenario {
|
||
background: var(--vp-c-bg);
|
||
border: 1px solid var(--vp-c-divider);
|
||
border-radius: 8px;
|
||
padding: 1.5rem;
|
||
}
|
||
|
||
.user-query {
|
||
font-weight: bold;
|
||
margin-bottom: 1rem;
|
||
text-align: right;
|
||
background: var(--vp-c-bg-alt);
|
||
display: inline-block;
|
||
padding: 8px 12px;
|
||
border-radius: 12px 12px 0 12px;
|
||
margin-left: auto;
|
||
}
|
||
|
||
.model-response {
|
||
display: flex;
|
||
gap: 1rem;
|
||
margin-bottom: 1rem;
|
||
}
|
||
|
||
.avatar {
|
||
font-size: 2rem;
|
||
}
|
||
|
||
.bubble {
|
||
background: var(--vp-c-bg-alt);
|
||
padding: 1rem;
|
||
border-radius: 0 12px 12px 12px;
|
||
flex: 1;
|
||
position: relative;
|
||
}
|
||
|
||
.model-response.base .bubble {
|
||
border: 2px solid #ef4444;
|
||
}
|
||
|
||
.model-response.aligned .bubble {
|
||
border: 2px solid #10b981;
|
||
}
|
||
|
||
.analysis {
|
||
text-align: center;
|
||
margin-top: 1rem;
|
||
}
|
||
|
||
.bad-tag {
|
||
color: #ef4444;
|
||
font-weight: bold;
|
||
border: 1px solid #ef4444;
|
||
padding: 4px 8px;
|
||
border-radius: 4px;
|
||
}
|
||
|
||
.good-tag {
|
||
color: #10b981;
|
||
font-weight: bold;
|
||
border: 1px solid #10b981;
|
||
padding: 4px 8px;
|
||
border-radius: 4px;
|
||
}
|
||
|
||
@media (max-width: 640px) {
|
||
.chat-container {
|
||
flex-direction: column;
|
||
}
|
||
.arrow-divider {
|
||
writing-mode: horizontal-tb;
|
||
align-self: center;
|
||
margin: 10px 0;
|
||
}
|
||
.train-step {
|
||
flex-direction: column;
|
||
align-items: flex-start;
|
||
gap: 0.5rem;
|
||
}
|
||
|
||
button {
|
||
cursor: pointer;
|
||
padding: 6px 12px;
|
||
background-color: var(--vp-c-brand);
|
||
color: white;
|
||
border: none;
|
||
border-radius: 4px;
|
||
font-weight: 600;
|
||
transition: background-color 0.2s;
|
||
}
|
||
|
||
button:hover:not(:disabled) {
|
||
background-color: var(--vp-c-brand-dark);
|
||
}
|
||
|
||
button:disabled {
|
||
opacity: 0.5;
|
||
cursor: not-allowed;
|
||
}
|
||
|
||
.primary-btn {
|
||
padding: 8px 20px;
|
||
font-size: 1rem;
|
||
box-shadow: 0 2px 8px rgba(var(--vp-c-brand-rgb), 0.25);
|
||
display: flex;
|
||
align-items: center;
|
||
gap: 6px;
|
||
}
|
||
|
||
.primary-btn:hover:not(:disabled) {
|
||
transform: translateY(-1px);
|
||
box-shadow: 0 4px 12px rgba(var(--vp-c-brand-rgb), 0.35);
|
||
}
|
||
}
|
||
</style>
|