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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Grape Models | NLP to Terminal - T5 Expert Models & Semantic Router</title>
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<script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3361658259789683"
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<!-- SEO Optimization Meta Tags -->
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<meta name="description" content="A collection of T5 expert models hosted on Hugging Face focused on translating natural language queries into actionable terminal commands, routed by an intelligent semantic router.">
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<meta name="keywords" content="Grape Models, NLP to Terminal, CodeT5, Hugging Face collection, semantic router, bash translator, natural language commands, Docker fine-tuning, machine learning models">
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<meta name="author" content="Juan Raul Rodriguez Gil">
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<link rel="canonical" href="https://grape.jrodriiguezg.link/">
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<!-- Open Graph (Facebook / LinkedIn) -->
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<meta property="og:title" content="Grape Models | NLP to Terminal - T5 Expert Models & Semantic Router">
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<meta property="og:description" content="A collection of T5 expert models hosted on Hugging Face focused on translating natural language queries into actionable terminal commands, routed by an intelligent semantic router.">
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<meta property="og:type" content="website">
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<meta property="og:url" content="https://grape.jrodriiguezg.link/">
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<meta property="og:image" content="https://grape.jrodriiguezg.link/styles.css">
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<!-- Twitter Cards -->
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<meta name="twitter:card" content="summary">
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<meta name="twitter:title" content="Grape Models | NLP to Terminal - T5 Expert Models & Semantic Router">
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<meta name="twitter:description" content="A collection of T5 expert models hosted on Hugging Face focused on translating natural language queries into actionable terminal commands, routed by an intelligent semantic router.">
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"name": "Grape Models",
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"applicationCategory": "DeveloperApplication",
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"operatingSystem": "Cross-platform (Python, Hugging Face Transformers)",
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"description": "A collection of CodeT5 expert models hosted on Hugging Face focused on translating natural language into actionable terminal commands (BASH, Docker, Search, Network), dynamically routed by an intelligent semantic classifier.",
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"url": "https://grape.jrodriiguezg.link/",
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"codeRepository": "https://huggingface.co/collections/jrodriiguezg/grape-models",
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"author": {
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"@type": "Person",
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"name": "Juan Raul Rodriguez Gil",
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"url": "https://jrodriiguezg.link/"
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},
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"knowsAbout": [
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"Natural Language Processing",
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"Hugging Face Transformers",
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"CodeT5",
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"Semantic Routing",
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"Terminal Automation"
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]
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}
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</script>
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<link rel="stylesheet" href="styles.css">
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<link rel="preconnect" href="https://fonts.googleapis.com">
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<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
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<link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;800&family=Fira+Code&display=swap" rel="stylesheet">
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</head>
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<body>
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<div class="container">
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<!-- Hero Section -->
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<section class="hero">
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<div class="badge">NLP & Transformers</div>
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<h1>Grape Models</h1>
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<p>A collection of T5 expert models hosted on Hugging Face focused on translating natural language into actionable terminal commands, routed by an intelligent semantic categorizer.</p>
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<div class="hero-buttons">
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<a href="https://huggingface.co/collections/jrodriiguezg/grape-models" target="_blank" rel="noopener noreferrer" class="btn btn-primary">
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<svg width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zM9.5 9C9.5 8.17 10.17 7.5 11 7.5S12.5 8.17 12.5 9 11.83 10.5 11 10.5 9.5 9.83 9.5 9zm5 0c0-.83.67-1.5 1.5-1.5s1.5.67 1.5 1.5-.67 1.5-1.5 1.5-1.5-.67-1.5-1.5zm-2.5 7c-2.33 0-4.31-1.46-5.11-3.5h10.22C16.31 14.54 14.33 16 12 16z"/></svg>
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View Collection
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</a>
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<a href="https://jrodriiguezg.link" class="btn btn-secondary">Back to Portfolio</a>
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<a href="https://ko-fi.com/jrodriiguezg" target="_blank" rel="noopener noreferrer" class="btn btn-kofi">
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<svg width="20" height="20" viewBox="0 0 24 24" fill="currentColor"><path d="M23.881 8.948c-.773-4.085-4.859-4.593-4.859-4.593H.723c-.604 0-.679.798-.679.798s-.082 7.324-.022 11.822c.164 2.424 2.586 2.672 2.586 2.672s8.267-.023 11.966-.049c2.438-.426 2.683-2.566 2.658-3.734 4.352.24 7.422-2.831 6.649-6.916zm-11.062 3.511c-1.246 1.453-4.011 3.976-4.011 3.976s-.121.119-.31.023c-.076-.057-.108-.09-.108-.09-.443-.441-3.368-3.049-4.051-3.954-1.094-1.465-1.083-3.262 0-4.198 1.116-.916 2.79-.843 3.995.212.352.312.593.636.593.636s.202-.308.514-.6A4.331 4.331 0 0 1 12.87 8.07c1.196-.906 2.87-.978 3.996-.061 1.083.936 1.094 2.733 0 4.198z"/></svg>
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Support on Ko-fi
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</a>
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</div>
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</section>
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<!-- Architecture Section -->
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<section class="glass-panel">
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<h2 class="section-title">Architecture & Base Model</h2>
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<div class="grid-2">
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<div>
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<p style="color: var(--text-secondary); margin-bottom: 1rem;">The Grape Models ecosystem utilizes a Mixture of Experts (MoE) approach to maximize terminal command accuracy.</p>
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<ul class="tech-list">
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<li><strong>Base Foundation:</strong> All 4 expert models share the same foundation: <span style="font-family: 'Fira Code', monospace; color: var(--text-primary);">Salesforce/codet5-small</span>, allowing for lightweight execution.</li>
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<li><strong>Semantic Router:</strong> The <span style="font-family: 'Fira Code', monospace; color: var(--text-primary);">minilm-l12-grape-route</span> model acts as a traffic controller, evaluating the user's natural language input and routing it to the appropriate specialized expert.</li>
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<li><strong>Custom Fine-Tuning:</strong> Each expert was strictly fine-tuned on custom datasets using Google Colab instances for specialized tasks.</li>
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</ul>
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<a href="https://huggingface.co/jrodriiguezg/minilm-l12-grape-route" target="_blank" rel="noopener noreferrer" class="expert-link" style="margin-top: 1rem;">Explore the Router Model →</a>
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</div>
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<div class="code-block">
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<span class="code-comment"># NLP Pipeline Concept</span>
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<span class="code-keyword">def</span> process_input(text):
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<span class="code-comment"># 1. Router categorizes intent</span>
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category = router_model.predict(text)
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<span class="code-comment"># 2. Select specific Grape Expert</span>
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expert = load_expert(category)
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<span class="code-comment"># 3. Generate terminal command</span>
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command = expert.generate(text)
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<span class="code-keyword">return</span> command
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</div>
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</div>
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</section>
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<!-- The Experts -->
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<section class="glass-panel">
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<h2 class="section-title">The Expert Models</h2>
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<p style="color: var(--text-secondary); margin-bottom: 2rem;">Each model in the Grape family specializes in a specific domain of terminal operations.</p>
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<div class="experts-grid">
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<!-- Chardonnay -->
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<div class="expert-card">
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<h3>Chardonnay <span class="expert-tag">BASH</span></h3>
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<p>The generalist expert. Converts natural language instructions into standard bash commands for file management, networking, and system operations.</p>
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<a href="https://huggingface.co/jrodriiguezg/grape-chardonnay" target="_blank" rel="noopener noreferrer" class="expert-link">Model Card →</a>
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</div>
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<!-- Pinot -->
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<div class="expert-card">
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<h3>Pinot <span class="expert-tag">SEARCH</span></h3>
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<p>The search and retrieval expert. Enforces a strict Zero-Modification Policy (ignores install/delete requests), focusing exclusively on information retrieval using tools like <span style="font-family: 'Fira Code', monospace;">find</span>, <span style="font-family: 'Fira Code', monospace;">grep</span>, and <span style="font-family: 'Fira Code', monospace;">stat</span>.</p>
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<a href="https://huggingface.co/jrodriiguezg/grape-pinot" target="_blank" rel="noopener noreferrer" class="expert-link">Model Card →</a>
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</div>
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<!-- Malbec -->
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<div class="expert-card">
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<h3>Malbec <span class="expert-tag">DOCKER</span></h3>
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<p>The containerization expert. Fine-tuned for Docker and docker-compose workflows. Trained on the custom public dataset <span style="font-family: 'Fira Code', monospace; color: var(--text-primary);">malbec-nl2docker-es</span>.</p>
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<div style="display: flex; gap: 1rem;">
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<a href="https://huggingface.co/jrodriiguezg/grape-malbec" target="_blank" rel="noopener noreferrer" class="expert-link">Model Card →</a>
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<a href="https://huggingface.co/datasets/jrodriiguezg/malbec-nl2docker-es" target="_blank" rel="noopener noreferrer" class="expert-link">Dataset →</a>
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</div>
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</div>
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<!-- Syrah -->
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<div class="expert-card">
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<h3>Syrah <span class="expert-tag">NETWORK</span></h3>
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<p>The network expert. Specifically trained to handle network-specific tasks including port scanning, connectivity testing, DNS resolution, and firewall configuration. It understands complex context constraints.</p>
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<a href="https://huggingface.co/jrodriiguezg/grape-syrah" target="_blank" rel="noopener noreferrer" class="expert-link">Model Card →</a>
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</div>
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</div>
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</section>
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<footer>
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<p>© 2026 Juan Raul Rodriguez Gil. <br> <a href="https://jrodriiguezg.link" class="footer-link">jrodriiguezg.link</a> | <a href="https://www.instagram.com/jrodriiguezg.link/?hl=es" target="_blank" rel="noopener noreferrer" class="footer-link">Instagram</a></p>
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</footer>
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:root {
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--text-secondary: #94a3b8;
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overflow-x: hidden;
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position: relative;
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animation: drift 20s infinite alternate;
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font-size: 1.25rem;
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|
||||||
|
border-color: rgba(255, 255, 255, 0.15);
|
||||||
|
transform: translateY(-2px);
|
||||||
|
}
|
||||||
|
|
||||||
|
.section-title {
|
||||||
|
font-size: 2rem;
|
||||||
|
margin-bottom: 2rem;
|
||||||
|
color: var(--text-primary);
|
||||||
|
font-weight: 700;
|
||||||
|
display: flex;
|
||||||
|
align-items: center;
|
||||||
|
gap: 1rem;
|
||||||
|
}
|
||||||
|
|
||||||
|
.section-title::after {
|
||||||
|
content: '';
|
||||||
|
flex: 1;
|
||||||
|
height: 1px;
|
||||||
|
background: linear-gradient(90deg, var(--border-color) 0%, transparent 100%);
|
||||||
|
}
|
||||||
|
|
||||||
|
/* Architecture / Grid */
|
||||||
|
.grid-2 {
|
||||||
|
display: grid;
|
||||||
|
grid-template-columns: 1fr 1fr;
|
||||||
|
gap: 3rem;
|
||||||
|
align-items: center;
|
||||||
|
}
|
||||||
|
|
||||||
|
.tech-list {
|
||||||
|
list-style: none;
|
||||||
|
margin-top: 1.5rem;
|
||||||
|
}
|
||||||
|
|
||||||
|
.tech-list li {
|
||||||
|
position: relative;
|
||||||
|
padding-left: 1.5rem;
|
||||||
|
margin-bottom: 1rem;
|
||||||
|
color: var(--text-secondary);
|
||||||
|
font-size: 1.05rem;
|
||||||
|
line-height: 1.6;
|
||||||
|
}
|
||||||
|
|
||||||
|
.tech-list li::before {
|
||||||
|
content: '';
|
||||||
|
position: absolute;
|
||||||
|
left: 0;
|
||||||
|
top: 0.6rem;
|
||||||
|
width: 8px;
|
||||||
|
height: 8px;
|
||||||
|
background: var(--accent-color);
|
||||||
|
border-radius: 50%;
|
||||||
|
box-shadow: 0 0 10px var(--accent-color);
|
||||||
|
}
|
||||||
|
|
||||||
|
.code-block {
|
||||||
|
background-color: #0d0f12;
|
||||||
|
padding: 1.5rem;
|
||||||
|
border-radius: 8px;
|
||||||
|
font-family: 'Fira Code', monospace;
|
||||||
|
font-size: 0.9rem;
|
||||||
|
color: #e2e8f0;
|
||||||
|
border: 1px solid var(--border-color);
|
||||||
|
overflow-x: auto;
|
||||||
|
white-space: pre-wrap;
|
||||||
|
word-break: break-word;
|
||||||
|
}
|
||||||
|
|
||||||
|
.code-comment {
|
||||||
|
color: #64748b;
|
||||||
|
}
|
||||||
|
.code-keyword {
|
||||||
|
color: #c678dd;
|
||||||
|
}
|
||||||
|
.code-string {
|
||||||
|
color: #98c379;
|
||||||
|
}
|
||||||
|
|
||||||
|
/* Experts Grid */
|
||||||
|
.experts-grid {
|
||||||
|
display: grid;
|
||||||
|
grid-template-columns: repeat(2, 1fr);
|
||||||
|
gap: 2rem;
|
||||||
|
}
|
||||||
|
|
||||||
|
.expert-card {
|
||||||
|
background: var(--bg-secondary);
|
||||||
|
border: 1px solid var(--border-color);
|
||||||
|
border-radius: 12px;
|
||||||
|
padding: 2rem;
|
||||||
|
transition: all 0.3s;
|
||||||
|
display: flex;
|
||||||
|
flex-direction: column;
|
||||||
|
}
|
||||||
|
|
||||||
|
.expert-card:hover {
|
||||||
|
border-color: var(--accent-color);
|
||||||
|
background: var(--bg-tertiary);
|
||||||
|
transform: translateY(-5px);
|
||||||
|
box-shadow: 0 10px 20px rgba(0, 0, 0, 0.2);
|
||||||
|
}
|
||||||
|
|
||||||
|
.expert-card h3 {
|
||||||
|
color: var(--text-primary);
|
||||||
|
margin-bottom: 0.5rem;
|
||||||
|
font-size: 1.4rem;
|
||||||
|
display: flex;
|
||||||
|
align-items: center;
|
||||||
|
justify-content: space-between;
|
||||||
|
}
|
||||||
|
|
||||||
|
.expert-tag {
|
||||||
|
font-family: 'Fira Code', monospace;
|
||||||
|
font-size: 0.75rem;
|
||||||
|
background-color: rgba(147, 51, 234, 0.15);
|
||||||
|
color: var(--accent-color);
|
||||||
|
padding: 0.3rem 0.6rem;
|
||||||
|
border-radius: 4px;
|
||||||
|
}
|
||||||
|
|
||||||
|
.expert-card p {
|
||||||
|
color: var(--text-secondary);
|
||||||
|
font-size: 0.95rem;
|
||||||
|
margin-bottom: 1.5rem;
|
||||||
|
flex: 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
.expert-link {
|
||||||
|
color: var(--accent-color);
|
||||||
|
text-decoration: none;
|
||||||
|
font-weight: 600;
|
||||||
|
font-size: 0.9rem;
|
||||||
|
display: inline-flex;
|
||||||
|
align-items: center;
|
||||||
|
gap: 0.4rem;
|
||||||
|
transition: color 0.2s;
|
||||||
|
}
|
||||||
|
|
||||||
|
.expert-link:hover {
|
||||||
|
color: var(--accent-hover);
|
||||||
|
}
|
||||||
|
|
||||||
|
/* Footer */
|
||||||
|
footer {
|
||||||
|
text-align: center;
|
||||||
|
color: var(--text-secondary);
|
||||||
|
font-size: 0.9rem;
|
||||||
|
padding: 3rem 0 2rem;
|
||||||
|
border-top: 1px solid var(--border-color);
|
||||||
|
margin-top: 5rem;
|
||||||
|
}
|
||||||
|
|
||||||
|
.footer-link {
|
||||||
|
color: var(--accent-color);
|
||||||
|
text-decoration: none;
|
||||||
|
transition: color 0.3s;
|
||||||
|
}
|
||||||
|
|
||||||
|
.footer-link:hover {
|
||||||
|
color: var(--text-primary);
|
||||||
|
}
|
||||||
|
|
||||||
|
/* Responsive */
|
||||||
|
@media (max-width: 900px) {
|
||||||
|
.grid-2 {
|
||||||
|
grid-template-columns: 1fr;
|
||||||
|
}
|
||||||
|
|
||||||
|
.experts-grid {
|
||||||
|
grid-template-columns: 1fr;
|
||||||
|
}
|
||||||
|
|
||||||
|
.hero h1 {
|
||||||
|
font-size: 3rem;
|
||||||
|
}
|
||||||
|
|
||||||
|
.glass-panel {
|
||||||
|
padding: 2rem;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
/* Estilo específico para Ko-fi en Grape Models */
|
||||||
|
.btn-kofi {
|
||||||
|
background-color: var(--glass-bg);
|
||||||
|
color: var(--text-primary);
|
||||||
|
border: 1px solid var(--border-color);
|
||||||
|
backdrop-filter: blur(10px);
|
||||||
|
}
|
||||||
|
|
||||||
|
/* Hover con resplandor morado (Grape purple) */
|
||||||
|
.btn-kofi:hover {
|
||||||
|
background-color: rgba(147, 51, 234, 0.15);
|
||||||
|
border-color: var(--accent-color);
|
||||||
|
color: #fff;
|
||||||
|
transform: translateY(-2px);
|
||||||
|
box-shadow: 0 4px 15px rgba(147, 51, 234, 0.2);
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user