1263 lines
33 KiB
Vue
1263 lines
33 KiB
Vue
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<!--
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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><strong>LLM 的本能是“续写”</strong>：它并不懂对话，只是根据上文猜下一个词。</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 type="text" v-model="completionInput" placeholder="Enter text..." :disabled="isGenerating">
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<button class="primary-btn" @click="runCompletion" :disabled="isGenerating || !completionInput">
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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><strong>如何让它对话？</strong> 我们用“剧本”包装输入，让模型以为自己在续写一段对话。</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 v-model="chatInput" placeholder="Say hello..." @keyup.enter="runChat">
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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><strong>Training (训练原理)</strong>: 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 Loss。</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">Step {{ currentStep }}/{{ totalSteps }}</span>
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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 v-if="currentStep === 0" class="content-box input placeholder">
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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">"{{ currentTrainData.input }}"</span>
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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 v-for="n in 5" :key="n" class="matrix-cell" :style="{ opacity: inputEmbeddingOpacities[n - 1] ?? 0.6, transform: `scaleY(${inputEmbeddingOpacities[n - 1] ?? 1})` }"></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 class="content-box pred" :class="{ correct: isPredictionCorrect }">
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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 v-for="n in 5" :key="n" class="matrix-cell pred-cell" :style="{ opacity: predEmbeddingOpacities[n - 1] ?? 0.6 }"></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 v-for="n in 5" :key="n" class="matrix-cell target-cell" :style="{ opacity: targetEmbeddingOpacities[n - 1] ?? 0.9 }"></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 class="loss-val-badge" :style="{ backgroundColor: getLossColor(currentLoss) }">Loss: {{ currentLoss.toFixed(4) }}</span>
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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 class="loss-bar-fill" :style="{ width: Math.min((currentLoss / 3) * 100, 100) + '%', backgroundColor: getLossColor(currentLoss) }"></div>
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</div>
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<div class="loss-feedback" :class="{ success: isPredictionCorrect, error: !isPredictionCorrect }">
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{{ isPredictionCorrect ? '✅ Parameters Good' : '❌ Update Weights' }}
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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 id="grid" width="30" height="30" patternUnits="userSpaceOnUse">
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<path d="M 30 0 L 0 0 0 30" fill="none" stroke="var(--vp-c-divider)" stroke-width="0.5" stroke-opacity="0.3"/>
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</pattern>
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<linearGradient id="chartGradient" x1="0" x2="0" y1="0" y2="1">
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<stop offset="0%" stop-color="var(--vp-c-brand)" stop-opacity="0.2"/>
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<stop offset="100%" stop-color="var(--vp-c-brand)" stop-opacity="0"/>
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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 x1="20" y1="130" x2="290" y2="130" stroke="var(--vp-c-text-3)" stroke-width="1" />
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<line x1="20" y1="10" x2="20" y2="130" stroke="var(--vp-c-text-3)" stroke-width="1" />
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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">Waiting for training to start...</div>
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<div v-for="(log, idx) in trainingLogs" :key="idx" class="log-item">
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<span class="log-step">[Step {{ String(log.step).padStart(2, '0') }}]</span>
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<span class="log-loss" :style="{ color: getLossColor(log.loss) }">Loss={{ log.loss.toFixed(2) }}</span>
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<span class="log-detail">{{ log.input }} -> <span :class="{ 'text-green': log.pred === log.target, 'text-red': log.pred !== log.target }">{{ log.pred }}</span></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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<div class="action-bar">
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<button class="train-btn" @click="handleTrainClick" :class="{ 'is-restart': currentStep >= totalSteps }">
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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">🔄</span>
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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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|||
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<!-- Tab 4: 进阶 - 微调与对齐 (RLHF) -->
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|||
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<div v-if="currentTab === 'rlhf'" class="mode-view">
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|||
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<div class="desc-box">
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|||
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<p><strong>从“胡说”到“好助手”</strong>：通过 RLHF (人类反馈) 让模型学会礼貌和安全。</p>
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</div>
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|||
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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 class="radio-option" :class="{ active: alignmentState === 'base' }">
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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 class="radio-option" :class="{ active: alignmentState === 'aligned' }">
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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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|
|||
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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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|||
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<div class="model-response" :class="alignmentState">
|
|||
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<div class="avatar">{{ alignmentState === 'base' ? '🤪' : '🤖' }}</div>
|
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<div class="bubble">
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|||
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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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|||
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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">⚠️ Unsafe / Not Helpful</span>
|
|||
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<span v-else class="good-tag">✅ Safe & Helpful</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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|
|||
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</div>
|
|||
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</div>
|
|||
|
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</template>
|
|||
|
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|
|||
|
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<script setup>
|
|||
|
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import { computed, ref } from 'vue'
|
|||
|
|
|
|||
|
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const currentTab = ref('completion')
|
|||
|
|
const tabs = [
|
|||
|
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{ id: 'completion', label: '1. 本能：续写', icon: '✍️' },
|
|||
|
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{ id: 'chat', label: '2. 技巧：对话', icon: '🎭' },
|
|||
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{ id: 'train', label: '3. 原理：训练', icon: '🧠' },
|
|||
|
|
{ id: 'rlhf', label: '4. 进阶：对齐', icon: '🛡️' }
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
// 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>
|