feat: comprehensive documentation and demo updates

- 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
This commit is contained in:
sanbuphy
2026-01-16 19:10:21 +08:00
parent c8567ce23f
commit 73f4788d7e
150 changed files with 19530 additions and 13401 deletions
@@ -6,8 +6,8 @@
<div class="ti-demo">
<!-- 顶部导航 -->
<div class="nav-tabs">
<button
v-for="tab in tabs"
<button
v-for="tab in tabs"
:key="tab.id"
:class="{ active: currentTab === tab.id }"
@click="currentTab = tab.id"
@@ -18,28 +18,39 @@
</div>
<div class="demo-content">
<!-- Tab 1: 基础能力 - 文本续写 -->
<div v-if="currentTab === 'completion'" class="mode-view">
<div class="desc-box">
<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>
<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>
</div>
<div class="interactive-area">
<div class="input-row">
<span class="prompt-label">Prompt (提示词):</span>
<input type="text" v-model="completionInput" placeholder="Enter text..." :disabled="isGenerating">
<button class="primary-btn" @click="runCompletion" :disabled="isGenerating || !completionInput">
<input
type="text"
v-model="completionInput"
placeholder="Enter text..."
:disabled="isGenerating"
/>
<button
class="primary-btn"
@click="runCompletion"
:disabled="isGenerating || !completionInput"
>
Generate
</button>
</div>
<div class="result-box">
<span class="user-text">{{ completionInput }}</span>
<span class="ai-text typing">{{ completionOutput }}</span>
<span v-if="isGenerating" class="cursor">|</span>
</div>
<div class="explanation" v-if="completionOutput">
💡 模型在计算概率<code>P(blue | The sky is) = 90%</code>
</div>
@@ -49,7 +60,10 @@
<!-- Tab 2: 技巧 - 对话原理 (Template) -->
<div v-if="currentTab === 'chat'" class="mode-view">
<div class="desc-box">
<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>
<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>
</div>
<div class="chat-container">
@@ -61,7 +75,11 @@
<div class="msg bot" v-if="chatOutput">{{ chatOutput }}</div>
</div>
<div class="input-area">
<input v-model="chatInput" placeholder="Say hello..." @keyup.enter="runChat">
<input
v-model="chatInput"
placeholder="Say hello..."
@keyup.enter="runChat"
/>
<button @click="runChat" :disabled="isGenerating">Send</button>
</div>
</div>
@@ -71,13 +89,13 @@
<div class="model-view-half">
<div class="half-label">模型看到的 (Raw Prompt)</div>
<div class="raw-prompt">
<span class="sys-tag">&lt;|system|&gt;</span><br>
You are a helpful assistant.<br>
<span class="bot-tag">&lt;|assistant|&gt;</span><br>
我是 AI 助手你好<br>
<span class="user-tag">&lt;|user|&gt;</span><br>
{{ chatInput || '...' }}<br>
<span class="bot-tag">&lt;|assistant|&gt;</span><br>
<span class="sys-tag">&lt;|system|&gt;</span><br />
You are a helpful assistant.<br />
<span class="bot-tag">&lt;|assistant|&gt;</span><br />
我是 AI 助手你好<br />
<span class="user-tag">&lt;|user|&gt;</span><br />
{{ chatInput || '...' }}<br />
<span class="bot-tag">&lt;|assistant|&gt;</span><br />
<span class="ai-text typing">{{ chatOutput }}</span>
</div>
</div>
@@ -87,31 +105,50 @@
<!-- Tab 3: 原理 - 训练 (Training) -->
<div v-if="currentTab === 'train'" class="mode-view">
<div class="desc-box">
<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>
<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>
</div>
<div class="training-dashboard">
<!-- 左侧训练过程可视化 -->
<div class="train-process-panel card-panel">
<div class="panel-header">
<span class="step-badge">Step {{ currentStep }}/{{ totalSteps }}</span>
<span class="step-badge"
>Step {{ currentStep }}/{{ totalSteps }}</span
>
<span class="panel-title">Training Process</span>
</div>
<div class="data-flow">
<!-- Input Section -->
<div class="flow-stage input-stage">
<div class="stage-label">1. Input (输入)</div>
<div v-if="currentStep === 0" class="content-box input placeholder">
<div
v-if="currentStep === 0"
class="content-box input placeholder"
>
<span class="text-content">点击下方按钮开始训练</span>
</div>
<div v-else class="content-box input">
<span class="text-content">"{{ currentTrainData.input }}"</span>
<span class="text-content"
>"{{ currentTrainData.input }}"</span
>
</div>
<div class="matrix-viz">
<span class="matrix-label">Embedding:</span>
<div class="matrix-row">
<span v-for="n in 5" :key="n" class="matrix-cell" :style="{ opacity: inputEmbeddingOpacities[n - 1] ?? 0.6, transform: `scaleY(${inputEmbeddingOpacities[n - 1] ?? 1})` }"></span>
<span
v-for="n in 5"
:key="n"
class="matrix-cell"
:style="{
opacity: inputEmbeddingOpacities[n - 1] ?? 0.6,
transform: `scaleY(${inputEmbeddingOpacities[n - 1] ?? 1})`
}"
></span>
</div>
</div>
</div>
@@ -125,16 +162,26 @@
<!-- Prediction vs Target Section -->
<div v-if="currentStep > 0" class="flow-stage comparison">
<div class="stage-label">2. Prediction vs Target</div>
<div class="compare-row">
<div class="compare-item">
<span class="sub-label">Prediction</span>
<div class="content-box pred" :class="{ correct: isPredictionCorrect }">
<div
class="content-box pred"
:class="{ correct: isPredictionCorrect }"
>
"{{ currentPrediction || '...' }}"
</div>
<div class="matrix-viz small">
<div class="matrix-row">
<span v-for="n in 5" :key="n" class="matrix-cell pred-cell" :style="{ opacity: predEmbeddingOpacities[n - 1] ?? 0.6 }"></span>
<span
v-for="n in 5"
:key="n"
class="matrix-cell pred-cell"
:style="{
opacity: predEmbeddingOpacities[n - 1] ?? 0.6
}"
></span>
</div>
</div>
</div>
@@ -148,7 +195,14 @@
</div>
<div class="matrix-viz small">
<div class="matrix-row">
<span v-for="n in 5" :key="n" class="matrix-cell target-cell" :style="{ opacity: targetEmbeddingOpacities[n - 1] ?? 0.9 }"></span>
<span
v-for="n in 5"
:key="n"
class="matrix-cell target-cell"
:style="{
opacity: targetEmbeddingOpacities[n - 1] ?? 0.9
}"
></span>
</div>
</div>
</div>
@@ -159,14 +213,34 @@
<div v-if="currentStep > 0" class="flow-stage loss-stage">
<div class="stage-header">
<span class="stage-label">3. Loss Calculation</span>
<span class="loss-val-badge" :style="{ backgroundColor: getLossColor(currentLoss) }">Loss: {{ currentLoss.toFixed(4) }}</span>
<span
class="loss-val-badge"
:style="{ backgroundColor: getLossColor(currentLoss) }"
>Loss: {{ currentLoss.toFixed(4) }}</span
>
</div>
<div class="loss-bar-container">
<div class="loss-bar-bg">
<div class="loss-bar-fill" :style="{ width: Math.min((currentLoss / 3) * 100, 100) + '%', backgroundColor: getLossColor(currentLoss) }"></div>
<div
class="loss-bar-fill"
:style="{
width: Math.min((currentLoss / 3) * 100, 100) + '%',
backgroundColor: getLossColor(currentLoss)
}"
></div>
</div>
<div class="loss-feedback" :class="{ success: isPredictionCorrect, error: !isPredictionCorrect }">
{{ isPredictionCorrect ? '✅ Parameters Good' : '❌ Update Weights' }}
<div
class="loss-feedback"
:class="{
success: isPredictionCorrect,
error: !isPredictionCorrect
}"
>
{{
isPredictionCorrect
? '✅ Parameters Good'
: '❌ Update Weights'
}}
</div>
</div>
</div>
@@ -182,35 +256,74 @@
<svg viewBox="0 0 300 150" class="loss-chart">
<!-- Background Grid -->
<defs>
<pattern id="grid" width="30" height="30" patternUnits="userSpaceOnUse">
<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"/>
<pattern
id="grid"
width="30"
height="30"
patternUnits="userSpaceOnUse"
>
<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"
/>
</pattern>
<linearGradient id="chartGradient" x1="0" x2="0" y1="0" y2="1">
<stop offset="0%" stop-color="var(--vp-c-brand)" stop-opacity="0.2"/>
<stop offset="100%" stop-color="var(--vp-c-brand)" stop-opacity="0"/>
<linearGradient
id="chartGradient"
x1="0"
x2="0"
y1="0"
y2="1"
>
<stop
offset="0%"
stop-color="var(--vp-c-brand)"
stop-opacity="0.2"
/>
<stop
offset="100%"
stop-color="var(--vp-c-brand)"
stop-opacity="0"
/>
</linearGradient>
</defs>
<rect width="100%" height="100%" fill="url(#grid)" />
<!-- Axes -->
<line x1="20" y1="130" x2="290" y2="130" stroke="var(--vp-c-text-3)" stroke-width="1" />
<line x1="20" y1="10" x2="20" y2="130" stroke="var(--vp-c-text-3)" stroke-width="1" />
<line
x1="20"
y1="130"
x2="290"
y2="130"
stroke="var(--vp-c-text-3)"
stroke-width="1"
/>
<line
x1="20"
y1="10"
x2="20"
y2="130"
stroke="var(--vp-c-text-3)"
stroke-width="1"
/>
<!-- Fill Area -->
<polygon
<polygon
v-if="lossPolylinePoints"
:points="`20,130 ${lossPolylinePoints} ${lossPolylinePoints.split(' ').pop().split(',')[0]},130`"
fill="url(#chartGradient)"
:points="`20,130 ${lossPolylinePoints} ${lossPolylinePoints.split(' ').pop().split(',')[0]},130`"
fill="url(#chartGradient)"
/>
<!-- The Line -->
<polyline
fill="none"
stroke="var(--vp-c-brand)"
stroke-width="2.5"
<polyline
fill="none"
stroke="var(--vp-c-brand)"
stroke-width="2.5"
stroke-linecap="round"
stroke-linejoin="round"
:points="lossPolylinePoints"
:points="lossPolylinePoints"
/>
</svg>
<div class="chart-labels">
@@ -219,7 +332,7 @@
<span>Step {{ totalSteps }}</span>
</div>
</div>
<div class="log-console-container">
<div class="console-header">
<div class="window-dots">
@@ -230,21 +343,48 @@
<span class="console-title">training_log.txt</span>
</div>
<div class="log-console">
<div v-if="trainingLogs.length === 0" class="log-placeholder">Waiting for training to start...</div>
<div v-for="(log, idx) in trainingLogs" :key="idx" class="log-item">
<span class="log-step">[Step {{ String(log.step).padStart(2, '0') }}]</span>
<span class="log-loss" :style="{ color: getLossColor(log.loss) }">Loss={{ log.loss.toFixed(2) }}</span>
<span class="log-detail">{{ log.input }} -> <span :class="{ 'text-green': log.pred === log.target, 'text-red': log.pred !== log.target }">{{ log.pred }}</span></span>
<div v-if="trainingLogs.length === 0" class="log-placeholder">
Waiting for training to start...
</div>
<div
v-for="(log, idx) in trainingLogs"
:key="idx"
class="log-item"
>
<span class="log-step"
>[Step {{ String(log.step).padStart(2, '0') }}]</span
>
<span
class="log-loss"
:style="{ color: getLossColor(log.loss) }"
>Loss={{ log.loss.toFixed(2) }}</span
>
<span class="log-detail"
>{{ log.input }} ->
<span
:class="{
'text-green': log.pred === log.target,
'text-red': log.pred !== log.target
}"
>{{ log.pred }}</span
></span
>
</div>
</div>
</div>
</div>
</div>
<div class="action-bar">
<button class="train-btn" @click="handleTrainClick" :class="{ 'is-restart': currentStep >= totalSteps }">
<button
class="train-btn"
@click="handleTrainClick"
:class="{ 'is-restart': currentStep >= totalSteps }"
>
<span class="btn-icon" v-if="currentStep === 0">🚀</span>
<span class="btn-icon" v-else-if="currentStep >= totalSteps">🔄</span>
<span class="btn-icon" v-else-if="currentStep >= totalSteps"
>🔄</span
>
<span class="btn-icon" v-else></span>
{{ trainButtonText }}
</button>
@@ -254,19 +394,28 @@
<!-- Tab 4: 进阶 - 微调与对齐 (RLHF) -->
<div v-if="currentTab === 'rlhf'" class="mode-view">
<div class="desc-box">
<p><strong>胡说好助手</strong>通过 RLHF (人类反馈) 让模型学会礼貌和安全</p>
<p>
<strong>胡说好助手</strong>通过 RLHF (人类反馈)
让模型学会礼貌和安全
</p>
</div>
<div class="alignment-demo">
<div class="controls">
<div class="radio-group">
<span class="group-label">模型状态</span>
<label class="radio-option" :class="{ active: alignmentState === 'base' }">
<input type="radio" v-model="alignmentState" value="base">
<label
class="radio-option"
:class="{ active: alignmentState === 'base' }"
>
<input type="radio" v-model="alignmentState" value="base" />
Base Model (未对齐)
</label>
<label class="radio-option" :class="{ active: alignmentState === 'aligned' }">
<input type="radio" v-model="alignmentState" value="aligned">
<label
class="radio-option"
:class="{ active: alignmentState === 'aligned' }"
>
<input type="radio" v-model="alignmentState" value="aligned" />
Aligned Model (已对齐)
</label>
</div>
@@ -274,9 +423,11 @@
<div class="scenario">
<div class="user-query">User: "如何制造混乱?"</div>
<div class="model-response" :class="alignmentState">
<div class="avatar">{{ alignmentState === 'base' ? '🤪' : '🤖' }}</div>
<div class="avatar">
{{ alignmentState === 'base' ? '🤪' : '🤖' }}
</div>
<div class="bubble">
<div v-if="alignmentState === 'base'">
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
@@ -288,13 +439,14 @@
</div>
<div class="analysis">
<span v-if="alignmentState === 'base'" class="bad-tag"> Unsafe / Not Helpful</span>
<span v-if="alignmentState === 'base'" class="bad-tag"
> Unsafe / Not Helpful</span
>
<span v-else class="good-tag"> Safe & Helpful</span>
</div>
</div>
</div>
</div>
</div>
</div>
</template>
@@ -319,10 +471,10 @@ 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))
await new Promise((r) => setTimeout(r, 50))
completionOutput.value += char
}
isGenerating.value = false
@@ -336,12 +488,16 @@ 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 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))
await new Promise((r) => setTimeout(r, 50))
chatOutput.value += char
}
isGenerating.value = false
@@ -383,9 +539,18 @@ const resetTrainingState = () => {
}
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)
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 = () => {
@@ -394,7 +559,8 @@ const handleTrainClick = () => {
}
if (!activeTrainData.value) {
activeTrainData.value = trainDataset[Math.floor(Math.random() * trainDataset.length)]
activeTrainData.value =
trainDataset[Math.floor(Math.random() * trainDataset.length)]
}
currentStep.value += 1
@@ -402,57 +568,57 @@ const handleTrainClick = () => {
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
}
// 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,
@@ -460,7 +626,7 @@ const handleTrainClick = () => {
pred: pred,
target: data.target
})
if (trainingLogs.value.length > 5) trainingLogs.value.pop()
}
@@ -471,20 +637,31 @@ const trainButtonText = computed(() => {
})
const getRandomWord = () => {
const words = ['cat', 'fly', 'run', 'red', 'table', 'what', 'bad', '未知', '乱码', '错误']
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 endY = 10 // Top (Loss = maxLoss)
const width = endX - startX
const height = startY - endY
const maxLoss = 3.5
@@ -499,18 +676,20 @@ const lossPolylinePoints = computed(() => {
// 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},${y}`
}).join(' ')
return lossHistory.value
.map((loss, idx) => {
// idx 0 corresponds to Step 1
// We want Step 1 to be at x=0? Or spread out?
// Let's spread out based on current progress or fixed totalSteps?
// Fixed totalSteps is better for visualization "filling up"
const stepIndex = idx // 0 to 9
const x = startX + (stepIndex / (totalSteps - 1)) * width
const y = startY - (loss / maxLoss) * height
return `${x},${y}`
})
.join(' ')
})
const getLossColor = (loss) => {
@@ -523,7 +702,6 @@ seedOpacities()
// Tab 4 Logic
const alignmentState = ref('base')
</script>
<style scoped>
@@ -633,7 +811,8 @@ const alignmentState = ref('base')
align-items: stretch;
}
.chat-ui-half, .model-view-half {
.chat-ui-half,
.model-view-half {
flex: 1;
display: flex;
flex-direction: column;
@@ -717,9 +896,15 @@ const alignmentState = ref('base')
max-height: 300px;
}
.sys-tag { color: #569cd6; }
.user-tag { color: #ce9178; }
.bot-tag { color: #4ec9b0; }
.sys-tag {
color: #569cd6;
}
.user-tag {
color: #ce9178;
}
.bot-tag {
color: #4ec9b0;
}
/* Tab 3 Styles (New) */
.training-dashboard {
@@ -815,7 +1000,7 @@ const alignmentState = ref('base')
display: flex;
align-items: center;
justify-content: center;
box-shadow: inset 0 2px 4px rgba(0,0,0,0.03);
box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.03);
}
.content-box.input.placeholder {
@@ -868,8 +1053,12 @@ const alignmentState = ref('base')
transform-origin: bottom;
}
.matrix-cell.pred-cell { background-color: #f59e0b; }
.matrix-cell.target-cell { background-color: #10b981; }
.matrix-cell.pred-cell {
background-color: #f59e0b;
}
.matrix-cell.target-cell {
background-color: #10b981;
}
/* Arrows */
.process-arrow {
@@ -952,7 +1141,9 @@ const alignmentState = ref('base')
margin-bottom: 0.8rem;
}
.stage-header .stage-label { margin-bottom: 0; }
.stage-header .stage-label {
margin-bottom: 0;
}
.loss-val-badge {
font-size: 0.75rem;
@@ -981,7 +1172,9 @@ const alignmentState = ref('base')
.loss-bar-fill {
height: 100%;
border-radius: 6px;
transition: width 0.4s ease, background-color 0.3s;
transition:
width 0.4s ease,
background-color 0.3s;
}
.loss-feedback {
@@ -993,8 +1186,14 @@ const alignmentState = ref('base')
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); }
.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 {
@@ -1027,7 +1226,7 @@ const alignmentState = ref('base')
background: #1e1e1e;
border-radius: 8px;
overflow: hidden;
box-shadow: 0 4px 12px rgba(0,0,0,0.2);
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.2);
}
.console-header {
@@ -1044,10 +1243,20 @@ const alignmentState = ref('base')
margin-right: 12px;
}
.dot { width: 10px; height: 10px; border-radius: 50%; }
.dot.red { background: #ff5f56; }
.dot.yellow { background: #ffbd2e; }
.dot.green { background: #27c93f; }
.dot {
width: 10px;
height: 10px;
border-radius: 50%;
}
.dot.red {
background: #ff5f56;
}
.dot.yellow {
background: #ffbd2e;
}
.dot.green {
background: #27c93f;
}
.console-title {
color: #888;
@@ -1079,11 +1288,28 @@ const alignmentState = ref('base')
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; }
.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 {
@@ -1223,40 +1449,40 @@ const alignmentState = ref('base')
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 {
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:hover:not(:disabled) {
background-color: var(--vp-c-brand-dark);
}
button:disabled {
opacity: 0.5;
cursor: not-allowed;
}
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 {
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);
}
.primary-btn:hover:not(:disabled) {
transform: translateY(-1px);
box-shadow: 0 4px 12px rgba(var(--vp-c-brand-rgb), 0.35);
}
}
</style>