gemma-4-E4B-it-The-DECKARD-Claude-Opus-Expresso-Universe-HERETIC-UNCENSORED-Thinking

74
4
license:apache-2.0
by
DavidAU
Image Model
OTHER
4B params
New
74 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
9GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
4GB+ RAM

Training Data Analysis

🟡 Average (4.3/10)

Researched training datasets used by gemma-4-E4B-it-The-DECKARD-Claude-Opus-Expresso-Universe-HERETIC-UNCENSORED-Thinking with quality assessment

Specialized For

general
science
multilingual
reasoning

Training Datasets (3)

common crawl
🔴 2.5/10
general
science
Key Strengths
  • •Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
  • •Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
  • •Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
  • •Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
  • •Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
wikipedia
🟡 5/10
science
multilingual
Key Strengths
  • •High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
  • •Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
  • •Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
  • •Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
  • •Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
arxiv
🟡 5.5/10
science
reasoning
Key Strengths
  • •Scientific Authority: Peer-reviewed content from established repository
  • •Domain-Specific: Specialized vocabulary and concepts
  • •Mathematical Content: Includes complex equations and notation
Considerations
  • •Specialized: Primarily technical and mathematical content
  • •English-Heavy: Predominantly English-language papers

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

🚀 The Complete Solution (Single File Example)htmlopenai
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Parallel AI Prompts</title>
    <style>
        body {
            font-family: sans-serif;
            padding: 20px;
            background-color: #f4f7f9;
        }
        .container {
            max-width: 1200px;
            margin: 0 auto;
        }
        .input-group {
            margin-bottom: 30px;
            display: flex;
            gap: 15px;
            align-items: center;
        }
        textarea {
            width: 100%;
            min-height: 100px;
            padding: 10px;
            border: 1px solid #ddd;
            border-radius: 5px;
            resize: none;
            box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
        }
        .button {
            padding: 10px 20px;
            background-color: #007bff;
            color: #fff;
            border: none;
            border-radius: 5px;
            cursor: pointer;
            transition: background-color 0.2s;
        }
        .button:disabled {
            background-color: #cccccc;
            cursor: not-allowed;
        }
        .results-grid {
            display: grid;
            grid-template-columns: repeat(4, 1fr);
            gap: 20px;
            margin-top: 40px;
        }
        .card {
            background: #ffffff;
            border: 1px solid #eee;
            border-radius: 8px;
            padding: 20px;
            box-shadow: 0 1px 3px rgba(0, 0, 0, 0.05);
        }
        .api-header {
            display: flex;
            justify-content: space-between;
            align-items: center;
            margin-bottom: 15px;
            padding-bottom: 15px;
            border-bottom: 1px solid #eee;
            font-size: 1.2em;
            font-weight: 600;
        }
        .status {
            display: flex;
            align-items: center;
            gap: 10px;
            font-size: 0.9em;
            color: #6c75ad;
        }
        .loading-indicator {
            border: 4px solid #f3f3f3;
            border-top-color: #333;
            border-right-color: #eee;
            border-bottom-color: #e8e8e8;
            border-left-color: #f7f7f9;
            border-radius: 2px;
            width: 100%;
            height: 20px;
            animation: spin 1s linear infinite;
        }
        @keyframes spin {
            to { transform: rotate(360deg); }
        }
        .response-container {
            background-color: #f8f9fa;
            padding: 15px;
            border-left: 3px solid #007bff;
            border-radius: 3px;
            min-height: 150px;
            white-space: pre-wrap;
        }
    </style>
</head>
<body>
    <div class="container">
        <h1>Execute 8 AI Prompts in Parallel</h1>

        <div class="input-group">
            <textarea id="promptInput" placeholder="Enter your prompt here..."></textarea>
            <button class="button" id="submitButton" onclick="processPrompt()">Send Prompts</button>
        </div>

        <div id="results-grid" class="results-grid">
            <!-- Results will be injected here -->
        </div>
    </div>

    <script>
    /**
     * 🌐 IMPORTANT SECURITY WARNING 🌐
     * 🚨 DO NOT HARDCODE API KEYS IN CLIENT-SIDE JS! 🚨
     * Anyone can view them in the browser's developer tools.
     * 💡 BEST PRACTICE: Use a PROXY SERVER (e.g., Next.js API layer, Cloudflare Worker)
     * to store keys and make requests from the backend.
     */

    // 🟢 Define your 8 API endpoints/keys here
    const API_KEYS = [
        "YOUR_API_KEY_1",
        "YOUR_API_KEY_2",
        "YOUR_API_KEY_3",
        "YOUR_API_KEY_4",
        "YOUR_API_KEY_5",
        "YOUR_API_KEY_6",
        "YOUR_API_KEY_7",
        "YOUR_API_KEY_8"
    ];

    // ====================================================
    // 💾 Function to handle API calls
    // ====================================================
    /**
     * Calls an AI API endpoint with the given prompt.
     * @param {string} prompt - The user's input query.
     * @param {string} apiKey - The API key for this specific service.
     * @returns {Promise<Response>} The final API response object.
     */
    async function callAI(prompt, apiKey) {
        const response = await fetch(`https://api.${apiKey}.openai.com/chat/completions?model=gpt-4-turbo&messages=[{"role":"user", "content":"${prompt}"}]`, {
            method: 'POST',
            headers: {
                'Authorization': `Bearer ${apiKey}`
            },
            body: JSON.stringify({ messages: [{ role: "user", content: "Please provide a comprehensive and detailed answer to the following prompt." }]})
        });

        if (!response.ok) {
            throw new Error(`API call failed: ${response.status} - ${response.statusText}`);
        }
        return response.json();
    }

    // ====================================================
    // 🚀 MAIN EXECUTION LOGIC
    // ====================================================
    async function processPrompt() {
        const promptText = document.getElementById('user-input').value.trim();
        const submitButton = document.getElementById('submit-button');
        const resultsContainer = document.getElementById('results-grid');
        
        if (!promptText) {
            alert("Please enter your prompt.");
            return;
        }

        // 1. Set loading state
        submitButton.disabled = true;
        submitButton.textContent = 'Loading 8 AI Responses...';
        resultsContainer.innerHTML = ''; // Clear previous results
        const loaderHtml = `<div class="loading-indicator" id="main-loader"></div>`;
        resultsContainer.innerHTML = loaderHtml;

        // 2. Create 8 concurrent API calls
        const apiPromises = API_KEYS.map((key, index) => 
            callAI(promptText, key)
        );

        // 3. Execute all calls simultaneously
        const results = Promise.allSettled(apiPromises);

        try {
            // 4. Handle all outcomes (success, failure, timeout)
            const finalResponses = await Promise.allSettled(results);
            
            // 5. Loop through all results and display them
            for (let i = 0; i < finalResponses.length; i++) {
                const result = finalResponses[i];
                const container = document.getElementById(`card-${i}`);
                const loadingIndicator = document.getElementById(`main-loader-${i}`);
                
                // Handle success
                if (result.status === 'fulfilled') {
                    const data = result.value;
                    const content = data.choices?.[0]?.message?.content || 'No response content found.';
                    const contentElement = container.querySelector('.response-text');
                    const statusElement = container.querySelector('.status-dot');

                    // Simulate streaming response
                    let text = '';
                    const typewriter = setInterval(() => {
                        if (text.length > 0) {
                            contentElement.innerText = text;
                            text += chunk;
                            const remaining = Math.floor(chunk.length * 0.02);
                            if (remaining > 0) {
                                typewriter.textContent = '...';
                            }
                        } else {
                            contentElement.innerText = 'Awaiting response...';
                            typewriter.textContent = '...';
                        }
                        // Simple rate limiting
                        setTimeout(typewriter, Math.max(5, Math.floor(remaining * 0.02)));
                    }, 20);
                } else {
                    // Handle failure
                    const errorText = `API Call Failed: ${error.message}`
                    contentElement.innerText = errorText;
                    statusElement.innerHTML = `<div class="status-dot error">❌ Error</div>`;
                }

            }
            
            // 6. Reset UI state
            submitButton.disabled = false;
            submitButton.textContent = 'Send Prompts';
            loadingIndicator.remove();

        } catch (error) {
            // Handle global execution failure
            const errorContainer = document.getElementById('global-error');
            if (errorContainer) {
                errorContainer.innerHTML = `<div style="color: red;">FATAL ERROR: ${error.message}</div>`;
            }
            submitButton.disabled = false;
            submitButton.textContent = 'Send Prompts';
        }
    }
    </script>

<div class="api-key-display" style="margin-top: 40px; padding: 20px; background-color: #f8f9fa; border: 1px solid #eee; border-radius: 8px;">
    <h3>🔑 IMPORTANT: API KEYS DISPLAYED IN SOURCE CODE</h3>
    <p>
        🛑 **SECURITY WARNING:** These keys are visible to anyone viewing the source code!
        <p style="color: #dc3515; font-weight: bold;">
            Replace all these with your REAL KEYS!
        </p>
        <div class="api-key-display" style="margin-top: 15px; padding: 15px; background-color: #ffebeb; border: 1px solid #fee6e0; border-radius: 5px; display: flex; flex-wrap: wrap; gap: 10px;">
                <!-- Keys will be injected here -->
            </div>
</div>
</body>
</html>

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