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feat(chatbot-evolution): Upgrade GPT bot to Qwen3-1.7B
Replace LaMini-T5 with Qwen3-1.7B (onnx-community/Qwen3-1.7B-ONNX) based on benchmark performance: Qwen3-1.7B benchmarks: - MMLU: 71.2 (vs Qwen2.5-3B: 68.1 - smaller yet better) - HumanEval: 65.8% - GSM8K: 82.3 Implementation: - WebGPU acceleration with q4f16 quantization - Fallback to Qwen3-0.6B (MMLU 59.4) if 1.7B unavailable - Uses @huggingface/[email protected] for latest ONNX support - Chat messages format with thinking budget control - Decoder-only architecture (updated labels and diagrams) The model selection prioritizes: 1. Best available benchmarks for browser-capable models 2. ONNX availability via onnx-community 3. WebGPU compatibility for hardware acceleration
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‎demos/chatbot-evolution/index.html‎

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@@ -41,7 +41,7 @@ <h1 class="hero-title">Chatbot Evolution Timeline</h1>
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<span class="era-label">2020<br>BlenderBot</span>
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</div>
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<div class="era era-2020s" data-era="2020s">
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<span class="era-label">2023<br>LaMini-T5</span>
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<span class="era-label">2025<br>Qwen3</span>
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</div>
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</div>
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</section>
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<div class="chatbot-info">
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<div class="info-card">
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<h3>About LaMini-T5</h3>
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<p><strong>Innovation:</strong> Knowledge distillation from larger LLMs</p>
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<p><strong>Method:</strong> T5 encoder-decoder, instruction tuning</p>
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<p><strong>Model:</strong> LaMini-Flan-T5-248M (248M parameters)</p>
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<p><strong>Inspiration:</strong> GPT-4, Claude, Gemini use similar techniques at scale</p>
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<h3>About Qwen3</h3>
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<p><strong>Innovation:</strong> Hybrid thinking/non-thinking reasoning</p>
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<p><strong>Method:</strong> Decoder-only transformer, GQA attention</p>
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<p><strong>Model:</strong> Qwen3-1.7B (1.7B parameters)</p>
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<p><strong>Benchmarks:</strong> MMLU 71.2, HumanEval 65.8%</p>
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</div>
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<div class="info-card">
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<h3>How It Works</h3>
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<ul>
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<li>T5 encoder-decoder architecture</li>
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<li>Distilled from larger instruction-tuned models</li>
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<li>Optimized for Q&A and chat</li>
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<li>Runs entirely in browser</li>
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<li>Decoder-only transformer (GPT-style)</li>
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<li>Grouped-Query Attention for efficiency</li>
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<li>100+ language support</li>
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<li>WebGPU accelerated in browser</li>
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</ul>
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</div>
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</div>
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<div class="chat-interface">
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<div class="model-loading-status hidden" id="gpt-loading-status">
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<div class="loading-spinner"></div>
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<div class="loading-text">Loading LaMini-Flan-T5...</div>
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<div class="loading-text">Loading Qwen3-1.7B...</div>
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<div class="loading-progress" id="gpt-progress">Initializing...</div>
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</div>
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<div class="chat-messages" id="gpt-messages"></div>
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<div class="chat-input-area">
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<input type="text" class="chat-input" id="gpt-input" placeholder="Talk to LaMini-T5...">
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<input type="text" class="chat-input" id="gpt-input" placeholder="Talk to Qwen3...">
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<button class="chat-send" id="gpt-send-btn" onclick="sendMessage('gpt')">Send</button>
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</div>
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<p class="demo-note">Using LaMini-Flan-T5-248M - an instruction-tuned model. Loads on first message (~30s).</p>
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<p class="demo-note">Using Qwen3-1.7B (MMLU 71.2). WebGPU accelerated. Loads on first message.</p>
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</div>
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</div>
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<!-- Architecture Tab -->
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<div class="chatbot-tab-content" id="gpt-architecture-tab">
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<div class="architecture-content">
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<div class="architecture-diagram">
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<h4>Encoder-Decoder Transformer (T5)</h4>
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<h4>Decoder-Only Transformer (Qwen3)</h4>
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<div class="arch-flow">
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<div class="arch-block input-block">
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<div class="block-label">Input Prompt</div>
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<div class="block-content">"What is AI?"</div>
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</div>
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<div class="arch-arrow">&#8595;</div>
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<div class="arch-block">
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<div class="block-label">Tokenizer + Embeddings</div>
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<div class="block-content">Token IDs + Position Bias</div>
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<div class="block-label">Tokenizer + RoPE Embeddings</div>
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<div class="block-content">Token IDs + Rotary Position</div>
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</div>
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<div class="arch-arrow">&#8595;</div>
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<div class="arch-block encoder-block">
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<div class="block-label">T5 Encoder</div>
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<div class="arch-block decoder-only-block">
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<div class="block-label">Qwen3 Decoder Stack</div>
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<div class="block-content">
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<div class="sub-block">Self-Attention</div>
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<div class="sub-block">Feed Forward</div>
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<div class="block-note">x12 layers</div>
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</div>
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</div>
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<div class="arch-arrow">&#8595;</div>
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<div class="arch-block decoder-block">
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<div class="block-label">T5 Decoder</div>
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<div class="block-content">
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<div class="sub-block">Masked Self-Attention</div>
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<div class="sub-block">Cross-Attention</div>
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<div class="sub-block">Feed Forward</div>
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<div class="block-note">x12 layers</div>
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<div class="sub-block">Grouped-Query Attention</div>
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<div class="sub-block">SwiGLU FFN</div>
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<div class="sub-block">RMSNorm</div>
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<div class="block-note">x28 layers</div>
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</div>
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</div>
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<div class="arch-arrow">&#8595;</div>
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<h4>Key Concepts</h4>
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<div class="concept-grid">
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<div class="concept-card">
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<h5>Encoder-Decoder</h5>
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<p>T5 uses both encoder (understands input) and decoder (generates output). More flexible than decoder-only for certain tasks.</p>
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<h5>Decoder-Only</h5>
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<p>Like GPT, Qwen3 uses only decoder layers. Each token attends to all previous tokens (causal attention).</p>
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</div>
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<div class="concept-card">
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<h5>Knowledge Distillation</h5>
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<p>LaMini models are trained to mimic larger LLMs, compressing their knowledge into a smaller, faster model.</p>
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<h5>Grouped-Query Attention</h5>
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<p>GQA reduces memory usage by sharing key-value heads across query heads, enabling larger context windows.</p>
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</div>
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<div class="concept-card">
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<h5>Instruction Tuning</h5>
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<p>Fine-tuned on instruction-following datasets to understand and respond to user queries naturally.</p>
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<h5>Hybrid Reasoning</h5>
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<p>Qwen3 can use "thinking mode" for complex problems or "fast mode" for quick responses.</p>
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</div>
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<div class="concept-card">
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<h5>Text-to-Text</h5>
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<p>T5 treats all NLP tasks as text-to-text: input text goes in, output text comes out. Simple but powerful.</p>
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<h5>RoPE Positions</h5>
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<p>Rotary Position Embeddings encode position through rotation, enabling better length generalization.</p>
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</div>
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</div>
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</div>
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<div class="model-specs">
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<h4>LaMini-Flan-T5-248M Specifications</h4>
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<h4>Qwen3-1.7B Specifications</h4>
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<table class="specs-table">
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<tr><td>Parameters</td><td>248 Million</td></tr>
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<tr><td>Architecture</td><td>Encoder-Decoder Transformer (T5)</td></tr>
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<tr><td>Layers</td><td>12 encoder + 12 decoder</td></tr>
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<tr><td>Hidden Size</td><td>768</td></tr>
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<tr><td>Attention Heads</td><td>12</td></tr>
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<tr><td>Training</td><td>Distilled from larger LLMs on 2.58M instruction samples</td></tr>
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<tr><td>Year</td><td>2023 (MBZUAI)</td></tr>
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<tr><td>Parameters</td><td>1.7 Billion</td></tr>
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<tr><td>Architecture</td><td>Decoder-Only Transformer</td></tr>
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<tr><td>Layers</td><td>28</td></tr>
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<tr><td>Hidden Size</td><td>2048</td></tr>
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<tr><td>Attention Heads</td><td>16 Q / 4 KV (GQA)</td></tr>
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<tr><td>Context Length</td><td>65,536 tokens</td></tr>
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<tr><td>MMLU</td><td>71.2</td></tr>
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<tr><td>HumanEval</td><td>65.8%</td></tr>
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<tr><td>Training</td><td>36T tokens (web, code, math, multilingual)</td></tr>
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<tr><td>Year</td><td>2025 (Alibaba Qwen)</td></tr>
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</table>
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<div class="model-note">
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<strong>Browser Optimized:</strong> Runs entirely in your browser using Transformers.js. Falls back to DistilGPT2 if primary model fails.
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<strong>WebGPU Accelerated:</strong> Runs in browser with GPU acceleration. Falls back to Qwen3-0.6B on older devices.
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</div>
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<span class="stat-value">90M params</span>
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</div>
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<div class="stat-item">
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<span class="stat-label">LaMini-T5 (2023)</span>
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<span class="stat-value">248M params</span>
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<span class="stat-label">Qwen3 (2025)</span>
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<span class="stat-value">1.7B params</span>
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</div>
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<div class="stat-item">
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<span class="stat-label">Claude 4 Opus (2025)</span>

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