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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
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<title>NeuralForge - CPU LLM Training</title>
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.terminal {
font-family: 'Courier New', monospace;
height: 300px;
overflow-y: auto;
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<body class="bg-gray-100 min-h-screen">
<div class="gradient-bg text-white">
<div class="container mx-auto px-4 py-6">
<header class="flex justify-between items-center">
<div class="flex items-center space-x-2">
<i class="fas fa-brain text-3xl"></i>
<h1 class="text-2xl font-bold">NeuralForge</h1>
</div>
<nav class="hidden md:flex space-x-6">
<a href="#" class="hover:text-blue-200">Dashboard</a>
<a href="#" class="hover:text-blue-200">Models</a>
<a href="#" class="hover:text-blue-200">Datasets</a>
<a href="#" class="hover:text-blue-200">Training</a>
<a href="#" class="hover:text-blue-200">Settings</a>
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<button class="md:hidden text-2xl">
<i class="fas fa-bars"></i>
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</header>
<div class="mt-12 mb-16 text-center">
<h2 class="text-4xl font-bold mb-4">CPU-Powered LLM Training</h2>
<p class="text-xl max-w-2xl mx-auto">Train language models efficiently using your CPU resources with our optimized training pipeline</p>
</div>
</div>
</div>
<div class="container mx-auto px-4 py-8 -mt-10">
<div class="bg-white rounded-xl shadow-lg p-6 mb-8">
<div class="flex flex-col md:flex-row gap-6">
<div class="md:w-2/3">
<h3 class="text-xl font-semibold mb-4">Training Configuration</h3>
<div class="grid grid-cols-1 md:grid-cols-2 gap-4 mb-6">
<div>
<label class="block text-sm font-medium text-gray-700 mb-1">Model Architecture</label>
<select class="w-full p-2 border border-gray-300 rounded-md focus:ring-2 focus:ring-blue-500 focus:border-blue-500">
<option>GPT-2 Small</option>
<option>GPT-2 Medium</option>
<option>BERT Base</option>
<option>DistilBERT</option>
<option>Custom...</option>
</select>
</div>
<div>
<label class="block text-sm font-medium text-gray-700 mb-1">Training Dataset</label>
<select class="w-full p-2 border border-gray-300 rounded-md focus:ring-2 focus:ring-blue-500 focus:border-blue-500">
<option>Wikipedia (English)</option>
<option>BookCorpus</option>
<option>OpenWebText</option>
<option>Custom Dataset</option>
</select>
</div>
</div>
<div class="grid grid-cols-1 md:grid-cols-3 gap-4 mb-6">
<div>
<label class="block text-sm font-medium text-gray-700 mb-1">Batch Size</label>
<input type="number" value="8" min="1" max="32" class="w-full p-2 border border-gray-300 rounded-md">
</div>
<div>
<label class="block text-sm font-medium text-gray-700 mb-1">Learning Rate</label>
<input type="number" step="0.00001" value="0.0001" class="w-full p-2 border border-gray-300 rounded-md">
</div>
<div>
<label class="block text-sm font-medium text-gray-700 mb-1">Epochs</label>
<input type="number" value="3" min="1" max="100" class="w-full p-2 border border-gray-300 rounded-md">
</div>
</div>
<div class="mb-6">
<label class="block text-sm font-medium text-gray-700 mb-2">CPU Threads (Max: <span id="maxThreads">8</span>)</label>
<input type="range" min="1" max="8" value="4" class="w-full" id="threadSlider">
<div class="flex justify-between text-xs text-gray-500">
<span>1</span>
<span>2</span>
<span>3</span>
<span>4</span>
<span>5</span>
<span>6</span>
<span>7</span>
<span>8</span>
</div>
</div>
<div class="flex space-x-4">
<button id="startTraining" class="px-6 py-2 bg-blue-600 text-white rounded-md hover:bg-blue-700 transition flex items-center">
<i class="fas fa-play mr-2"></i> Start Training
</button>
<button class="px-6 py-2 border border-gray-300 rounded-md hover:bg-gray-50 transition flex items-center">
<i class="fas fa-save mr-2"></i> Save Config
</button>
<button id="stopTraining" class="px-6 py-2 border border-red-300 text-red-600 rounded-md hover:bg-red-50 transition flex items-center hidden">
<i class="fas fa-stop mr-2"></i> Stop Training
</button>
</div>
</div>
<div class="md:w-1/3 bg-gray-50 p-4 rounded-lg">
<h3 class="text-lg font-semibold mb-4">System Resources</h3>
<div class="mb-4">
<div class="flex justify-between mb-1">
<span class="text-sm font-medium">CPU Utilization</span>
<span id="cpuUsage" class="text-sm">0%</span>
</div>
<div class="w-full bg-gray-200 rounded-full h-2.5">
<div id="cpuBar" class="bg-blue-600 h-2.5 rounded-full" style="width: 0%"></div>
</div>
</div>
<div class="mb-4">
<div class="flex justify-between mb-1">
<span class="text-sm font-medium">Memory Usage</span>
<span id="memUsage" class="text-sm">0 GB / 16 GB</span>
</div>
<div class="w-full bg-gray-200 rounded-full h-2.5">
<div id="memBar" class="bg-green-600 h-2.5 rounded-full" style="width: 0%"></div>
</div>
</div>
<div class="mb-4">
<div class="flex justify-between mb-1">
<span class="text-sm font-medium">Disk Cache</span>
<span id="diskUsage" class="text-sm">0 GB / 50 GB</span>
</div>
<div class="w-full bg-gray-200 rounded-full h-2.5">
<div id="diskBar" class="bg-purple-600 h-2.5 rounded-full" style="width: 0%"></div>
</div>
</div>
<div class="mt-6">
<h4 class="text-sm font-semibold mb-2">Estimated Training Time</h4>
<div class="bg-white p-3 rounded-md border border-gray-200">
<div class="flex items-center">
<i class="fas fa-clock text-blue-500 mr-2"></i>
<span id="estTime">Calculating...</span>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="grid grid-cols-1 md:grid-cols-2 gap-8 mb-8">
<div class="bg-white rounded-xl shadow-lg p-6">
<h3 class="text-xl font-semibold mb-4 flex items-center">
<i class="fas fa-terminal mr-2 text-blue-500"></i> Training Log
</h3>
<div id="terminal" class="terminal bg-black text-green-400 p-4 rounded-md font-mono text-sm overflow-y-auto">
<div>> Welcome to NeuralForge LLM Trainer</div>
<div>> System ready for training configuration</div>
<div>> CPU: 8 cores detected</div>
<div>> RAM: 16GB available</div>
</div>
</div>
<div class="bg-white rounded-xl shadow-lg p-6">
<h3 class="text-xl font-semibold mb-4 flex items-center">
<i class="fas fa-chart-line mr-2 text-blue-500"></i> Training Metrics
</h3>
<div class="grid grid-cols-2 gap-4 mb-4">
<div class="bg-gray-50 p-3 rounded-md">
<div class="text-sm text-gray-500">Loss</div>
<div id="lossValue" class="text-2xl font-bold">--</div>
</div>
<div class="bg-gray-50 p-3 rounded-md">
<div class="text-sm text-gray-500">Perplexity</div>
<div id="perplexityValue" class="text-2xl font-bold">--</div>
</div>
</div>
<div class="h-48 bg-gray-50 rounded-md flex items-center justify-center">
<p class="text-gray-400">Training metrics will appear here</p>
</div>
</div>
</div>
<div class="bg-white rounded-xl shadow-lg p-6 mb-8">
<h3 class="text-xl font-semibold mb-6">Available Model Architectures</h3>
<div class="grid grid-cols-1 md:grid-cols-3 gap-6">
<div class="model-card bg-gray-50 p-5 rounded-lg border border-gray-200 transition duration-300 cursor-pointer hover:border-blue-300">
<div class="flex justify-between items-start mb-3">
<h4 class="font-semibold">GPT-2 Small</h4>
<span class="bg-blue-100 text-blue-800 text-xs px-2 py-1 rounded">Recommended</span>
</div>
<p class="text-sm text-gray-600 mb-4">117M parameters, good for most tasks</p>
<div class="text-xs text-gray-500">
<div class="flex justify-between mb-1">
<span>CPU RAM Needed:</span>
<span>4GB</span>
</div>
<div class="flex justify-between">
<span>Training Time:</span>
<span>~12 hours</span>
</div>
</div>
</div>
<div class="model-card bg-gray-50 p-5 rounded-lg border border-gray-200 transition duration-300 cursor-pointer hover:border-blue-300">
<div class="flex justify-between items-start mb-3">
<h4 class="font-semibold">DistilBERT</h4>
</div>
<p class="text-sm text-gray-600 mb-4">66M parameters, distilled BERT model</p>
<div class="text-xs text-gray-500">
<div class="flex justify-between mb-1">
<span>CPU RAM Needed:</span>
<span>3GB</span>
</div>
<div class="flex justify-between">
<span>Training Time:</span>
<span>~8 hours</span>
</div>
</div>
</div>
<div class="model-card bg-gray-50 p-5 rounded-lg border border-gray-200 transition duration-300 cursor-pointer hover:border-blue-300">
<div class="flex justify-between items-start mb-3">
<h4 class="font-semibold">TinyLLAMA</h4>
</div>
<p class="text-sm text-gray-600 mb-4">28M parameters, lightweight option</p>
<div class="text-xs text-gray-500">
<div class="flex justify-between mb-1">
<span>CPU RAM Needed:</span>
<span>2GB</span>
</div>
<div class="flex justify-between">
<span>Training Time:</span>
<span>~5 hours</span>
</div>
</div>
</div>
</div>
</div>
</div>
<footer class="bg-gray-800 text-white py-8">
<div class="container mx-auto px-4">
<div class="flex flex-col md:flex-row justify-between">
<div class="mb-6 md:mb-0">
<h3 class="text-xl font-bold mb-4">NeuralForge</h3>
<p class="text-gray-400 max-w-md">Optimized CPU training for language models. No GPU required.</p>
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<h4 class="text-sm font-semibold uppercase tracking-wider mb-4">Product</h4>
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<li><a href="#" class="text-gray-400 hover:text-white">Features</a></li>
<li><a href="#" class="text-gray-400 hover:text-white">Pricing</a></li>
<li><a href="#" class="text-gray-400 hover:text-white">Documentation</a></li>
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<h4 class="text-sm font-semibold uppercase tracking-wider mb-4">Resources</h4>
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<li><a href="#" class="text-gray-400 hover:text-white">Blog</a></li>
<li><a href="#" class="text-gray-400 hover:text-white">Tutorials</a></li>
<li><a href="#" class="text-gray-400 hover:text-white">Support</a></li>
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<h4 class="text-sm font-semibold uppercase tracking-wider mb-4">Company</h4>
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<li><a href="#" class="text-gray-400 hover:text-white">About</a></li>
<li><a href="#" class="text-gray-400 hover:text-white">Careers</a></li>
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<p class="text-gray-400 text-sm">© 2023 NeuralForge. All rights reserved.</p>
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<a href="#" class="text-gray-400 hover:text-white"><i class="fab fa-twitter"></i></a>
<a href="#" class="text-gray-400 hover:text-white"><i class="fab fa-github"></i></a>
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<script>
// Simulate training process
let trainingInterval;
let isTraining = false;
let epoch = 0;
let totalEpochs = 3;
let loss = 0;
let perplexity = 0;
document.getElementById('startTraining').addEventListener('click', startTraining);
document.getElementById('stopTraining').addEventListener('click', stopTraining);
document.getElementById('threadSlider').addEventListener('input', updateThreads);
function updateThreads() {
const threads = document.getElementById('threadSlider').value;
document.getElementById('maxThreads').textContent = threads;
updateEstTime(threads);
}
function updateEstTime(threads) {
const baseTime = 12; // hours for GPT-2 Small with 4 threads
const estTime = (baseTime * 4 / threads).toFixed(1);
document.getElementById('estTime').textContent = `~${estTime} hours`;
}
function startTraining() {
if (isTraining) return;
isTraining = true;
document.getElementById('startTraining').classList.add('hidden');
document.getElementById('stopTraining').classList.remove('hidden');
const terminal = document.getElementById('terminal');
terminal.innerHTML += '<div>> Starting training process...</div>';
terminal.scrollTop = terminal.scrollHeight;
epoch = 0;
loss = 4.5;
perplexity = 120;
// Update metrics
document.getElementById('lossValue').textContent = loss.toFixed(4);
document.getElementById('perplexityValue').textContent = Math.round(perplexity);
// Simulate resource usage
let cpuUsage = 10;
let memUsage = 2;
let diskUsage = 5;
trainingInterval = setInterval(() => {
// Simulate CPU usage
cpuUsage = Math.min(100, cpuUsage + Math.random() * 10);
document.getElementById('cpuUsage').textContent = Math.round(cpuUsage) + '%';
document.getElementById('cpuBar').style.width = cpuUsage + '%';
// Simulate memory usage
memUsage = Math.min(16, memUsage + Math.random() * 0.5);
document.getElementById('memUsage').textContent = memUsage.toFixed(1) + ' GB / 16 GB';
document.getElementById('memBar').style.width = (memUsage / 16 * 100) + '%';
// Simulate disk usage
diskUsage = Math.min(50, diskUsage + Math.random() * 2);
document.getElementById('diskUsage').textContent = diskUsage.toFixed(1) + ' GB / 50 GB';
document.getElementById('diskBar').style.width = (diskUsage / 50 * 100) + '%';
// Update training metrics
if (Math.random() > 0.7) {
loss = Math.max(0.5, loss - Math.random() * 0.3);
perplexity = Math.max(10, perplexity - Math.random() * 5);
document.getElementById('lossValue').textContent = loss.toFixed(4);
document.getElementById('perplexityValue').textContent = Math.round(perplexity);
// Add to terminal log
if (Math.random() > 0.8) {
epoch++;
const progress = Math.min(100, Math.round((epoch / totalEpochs) * 100));
terminal.innerHTML += `<div>> Epoch ${epoch}/${totalEpochs} - Loss: ${loss.toFixed(4)} - Perplexity: ${Math.round(perplexity)}</div>`;
terminal.scrollTop = terminal.scrollHeight;
if (epoch >= totalEpochs) {
stopTraining();
terminal.innerHTML += '<div class="text-green-300">> Training completed successfully!</div>';
terminal.scrollTop = terminal.scrollHeight;
}
}
}
}, 1000);
}
function stopTraining() {
clearInterval(trainingInterval);
isTraining = false;
document.getElementById('startTraining').classList.remove('hidden');
document.getElementById('stopTraining').classList.add('hidden');
// Reset resource usage
document.getElementById('cpuUsage').textContent = '0%';
document.getElementById('cpuBar').style.width = '0%';
document.getElementById('memUsage').textContent = '0 GB / 16 GB';
document.getElementById('memBar').style.width = '0%';
document.getElementById('diskUsage').textContent = '0 GB / 50 GB';
document.getElementById('diskBar').style.width = '0%';
}
// Initialize
updateThreads();
// Model card selection
document.querySelectorAll('.model-card').forEach(card => {
card.addEventListener('click', function() {
document.querySelectorAll('.model-card').forEach(c => {
c.classList.remove('border-blue-500', 'bg-blue-50');
});
this.classList.add('border-blue-500', 'bg-blue-50');
});
});
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