155 lines
11 KiB
HTML
155 lines
11 KiB
HTML
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"name": "Grape Models",
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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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"Hugging Face Transformers",
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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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View Collection
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Support on Ko-fi
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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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