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<!DOCTYPE html>
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<title>RedHatAI Model Explorer</title>
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<h1 class="text-3xl font-bold text-gray-900">RedHatAI Model Explorer</h1>
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<a href="https://huggingface.co/RedHatAI" target="_blank" class="inline-flex items-center px-4 py-2 border border-transparent text-sm font-medium rounded-md shadow-sm text-white bg-red-600 hover:bg-red-700 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-red-500">
<i class="fab fa-hubspot mr-2"></i> View on Hugging Face
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<p class="mt-2 text-sm text-gray-600">Browse and filter through all models from the RedHatAI organization</p>
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<option value="all">All Models</option>
<option value="text-generation">Text Generation</option>
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<span class="tag px-3 py-1 rounded-full text-xs font-medium bg-gray-100 text-gray-800 cursor-pointer" data-tag="qwen">Qwen</span>
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<span class="tag px-3 py-1 rounded-full text-xs font-medium bg-gray-100 text-gray-800 cursor-pointer" data-tag="int4">4-bit</span>
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<span class="ml-3 text-gray-600">Loading models...</span>
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<div id="models-container" class="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-6 hidden">
<!-- Models will be inserted here by JavaScript -->
</div>
<div id="no-results" class="hidden text-center py-12">
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<h3 class="text-lg font-medium text-gray-900">No models found</h3>
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<script>
document.addEventListener('DOMContentLoaded', function() {
// DOM elements
const searchInput = document.getElementById('search');
const filterSelect = document.getElementById('filter');
const sortSelect = document.getElementById('sort');
const modelsContainer = document.getElementById('models-container');
const loadingElement = document.getElementById('loading');
const noResultsElement = document.getElementById('no-results');
const prevPageButton = document.getElementById('prev-page');
const nextPageButton = document.getElementById('next-page');
const pageInfoElement = document.getElementById('page-info');
const tags = document.querySelectorAll('.tag');
const statsContainer = document.getElementById('stats');
const totalDownloadsElement = document.getElementById('total-downloads');
const totalLikesElement = document.getElementById('total-likes');
const totalModelsElement = document.getElementById('total-models');
// State variables
let allModels = [];
let filteredModels = [];
let currentPage = 1;
const modelsPerPage = 9;
let activeTag = 'all';
// Initialize the app
fetchModels();
// Event listeners
searchInput.addEventListener('input', debounce(filterModels, 300));
filterSelect.addEventListener('change', filterModels);
sortSelect.addEventListener('change', filterModels);
prevPageButton.addEventListener('click', goToPreviousPage);
nextPageButton.addEventListener('click', goToNextPage);
tags.forEach(tag => tag.addEventListener('click', filterByTag));
// Fetch models from Hugging Face API
async function fetchModels() {
try {
loadingElement.classList.remove('hidden');
// In a real app, you would fetch from the API:
const response = await fetch('https://huggingface.co/api/models?author=RedHatAI');
allModels = await response.json();
// // For demo purposes, we'll use the sample data from your example
// allModels = [
// {
// "_id": "67f7a165ec6cd206ef5da57f",
// "id": "RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic",
// "likes": 6,
// "trendingScore": 6,
// "private": false,
// "downloads": 1232,
// "tags": ["safetensors", "llama4", "base_model:meta-llama/Llama-4-Scout-17B-16E-Instruct", "base_model:quantized:meta-llama/Llama-4-Scout-17B-16E-Instruct", "compressed-tensors", "region:us"],
// "createdAt": "2025-04-10T10:45:57.000Z",
// "modelId": "RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic",
// "pipeline_tag": "text-generation"
// },
// {
// "_id": "66a02afb813431cd7a6bca12",
// "id": "RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8",
// "likes": 45,
// "trendingScore": 3,
// "private": false,
// "downloads": 182800,
// "tags": ["transformers", "safetensors", "llama", "text-generation", "fp8", "vllm", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "base_model:meta-llama/Llama-3.1-70B-Instruct", "base_model:quantized:meta-llama/Llama-3.1-70B-Instruct", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "compressed-tensors", "region:us"],
// "pipeline_tag": "text-generation",
// "library_name": "transformers",
// "createdAt": "2024-07-23T22:13:15.000Z",
// "modelId": "RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8"
// },
// {
// "_id": "664b9a005df8c7926f78be84",
// "id": "RedHatAI/Meta-Llama-3-8B-Instruct-FP8-KV",
// "likes": 8,
// "trendingScore": 1,
// "private": false,
// "downloads": 3950,
// "tags": ["transformers", "safetensors", "llama", "text-generation", "fp8", "vllm", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us"],
// "pipeline_tag": "text-generation",
// "library_name": "transformers",
// "createdAt": "2025-05-20T18:44:16.000Z",
// "modelId": "RedHatAI/Meta-Llama-3-8B-Instruct-FP8-KV"
// },
// {
// "_id": "66a3f1c5ee3de8c56ef34fa3",
// "id": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",
// "likes": 27,
// "trendingScore": 1,
// "private": false,
// "downloads": 156851,
// "tags": ["transformers", "safetensors", "llama", "text-generation", "int4", "vllm", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:quantized:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us"],
// "pipeline_tag": "text-generation",
// "library_name": "transformers",
// "createdAt": "2024-07-26T18:58:13.000Z",
// "modelId": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16"
// },
// {
// "_id": "66aa715f1b4923ce7bb7d49d",
// "id": "RedHatAI/Meta-Llama-3.1-8B-quantized.w8a8",
// "likes": 3,
// "trendingScore": 1,
// "private": false,
// "downloads": 224,
// "tags": ["transformers", "safetensors", "llama", "text-generation", "int8", "vllm", "quantized", "8-bit", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2210.17323", "base_model:meta-llama/Llama-3.1-8B", "base_model:quantized:meta-llama/Llama-3.1-8B", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "compressed-tensors", "region:us"],
// "pipeline_tag": "text-generation",
// "library_name": "transformers",
// "createdAt": "2024-07-31T17:16:15.000Z",
// "modelId": "RedHatAI/Meta-Llama-3.1-8B-quantized.w8a8"
// },
// {
// "_id": "66be6896182d5a69aee95889",
// "id": "RedHatAI/gemma-2-9b-it-quantized.w4a16",
// "likes": 2,
// "trendingScore": 1,
// "private": false,
// "downloads": 88,
// "tags": ["safetensors", "gemma2", "text-generation", "conversational", "en", "arxiv:2210.17323", "license:llama2", "compressed-tensors", "region:us"],
// "pipeline_tag": "text-generation",
// "createdAt": "2024-08-15T20:44:06.000Z",
// "modelId": "RedHatAI/gemma-2-9b-it-quantized.w4a16"
// },
// {
// "_id": "678e9356b12fa7d661ccf013",
// "id": "RedHatAI/Llama-3.3-70B-Instruct-quantized.w8a8",
// "likes": 6,
// "trendingScore": 1,
// "private": false,
// "downloads": 4707,
// "tags": ["safetensors", "llama", "int8", "vllm", "chat", "neuralmagic", "llmcompressor", "text-generation", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "base_model:meta-llama/Llama-3.3-70B-Instruct", "base_model:quantized:meta-llama/Llama-3.3-70B-Instruct", "license:llama3.3", "8-bit", "compressed-tensors", "region:us"],
// "pipeline_tag": "text-generation",
// "createdAt": "2025-01-20T18:17:58.000Z",
// "modelId": "RedHatAI/Llama-3.3-70B-Instruct-quantized.w8a8"
// },
// {
// "_id": "679e94cd49b5b38e4fa2d8d0",
// "id": "RedHatAI/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic",
// "likes": 8,
// "trendingScore": 1,
// "private": false,
// "downloads": 135908,
// "tags": ["transformers", "safetensors", "llama", "text-generation", "deepseek", "fp8", "vllm", "conversational", "base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-70B", "base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Llama-70B", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "compressed-tensors", "region:us"],
// "pipeline_tag": "text-generation",
// "library_name": "transformers",
// "createdAt": "2025-02-01T21:40:29.000Z",
// "modelId": "RedHatAI/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic"
// },
// {
// "_id": "679becee4378dbc3579ac11a",
// "id": "RedHatAI/Mistral-Small-24B-Instruct-2501-FP8-dynamic",
// "private": false,
// "pipeline_tag": "text-generation",
// "library_name": "transformers",
// "tags": ["transformers", "safetensors", "mistral", "text-generation", "mistral-small", "fp8", "vllm", "conversational", "en", "base_model:mistralai/Mistral-Small-24B-Instruct-2501", "base_model:quantized:mistralai/Mistral-Small-24B-Instruct-2501", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "compressed-tensors", "region:us"],
// "downloads": 229986,
// "likes": 12,
// "modelId": "RedHatAI/Mistral-Small-24B-Instruct-2501-FP8-dynamic",
// "author": "RedHatAI",
// "sha": "9cb69aeda1e1162aa9c00c399105703c06214220",
// "lastModified": "2025-01-31T08:41:28.000Z",
// "gated": false,
// "disabled": false,
// "widgetData": [
// {"text": "Hi, what can you help me with?"},
// {"text": "What is 84 * 3 / 2?"},
// {"text": "Tell me an interesting fact about the universe!"},
// {"text": "Explain quantum computing in simple terms."}
// ],
// "config": {
// "architectures": ["MistralForCausalLM"],
// "model_type": "mistral",
// "quantization_config": {"quant_method": "compressed-tensors"},
// "tokenizer_config": {
// "bos_token": "<s>",
// "chat_template": "{%- set today = strftime_now(\"%Y-%m-%d\") %}\n{%- set default_system_message = \"You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.\\nYour knowledge base was last updated on 2023-10-01. The current date is \" + today + \".\\n\\nWhen you're not sure about some information, you say that you don't have the information and don't make up anything.\\nIf the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. \\\"What are some good restaurants around me?\\\" => \\\"Where are you?\\\" or \\\"When is the next flight to Tokyo\\\" => \\\"Where do you travel from?\\\")\" %}\n\n{{- bos_token }}\n\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set system_message = default_system_message %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{{- '[SYSTEM_PROMPT]' + system_message + '[/SYSTEM_PROMPT]' }}\n\n{%- for message in loop_messages %}\n {%- if message['role'] == 'user' %}\n {{- '[INST]' + message['content'] + '[/INST]' }}\n {%- elif message['role'] == 'system' %}\n {{- '[SYSTEM_PROMPT]' + message['content'] + '[/SYSTEM_PROMPT]' }}\n {%- elif message['role'] == 'assistant' %}\n {{- message['content'] + eos_token }}\n {%- else %}\n {{- raise_exception('Only user, system and assistant roles are supported!') }}\n {%- endif %}\n{%- endfor %}",
// "eos_token": "</s>",
// "unk_token": "<unk>",
// "use_default_system_prompt": false
// }
// },
// "cardData": {
// "license": "apache-2.0",
// "language": ["en"],
// "tags": ["mistral", "mistral-small", "fp8", "vllm"],
// "base_model": "mistralai/Mistral-Small-24B-Instruct-2501",
// "library_name": "transformers"
// },
// "transformersInfo": {
// "auto_model": "AutoModelForCausalLM",
// "pipeline_tag": "text-generation",
// "processor": "AutoTokenizer"
// },
// "siblings": [
// {"rfilename": ".gitattributes"},
// {"rfilename": "README.md"},
// {"rfilename": "config.json"},
// {"rfilename": "generation_config.json"},
// {"rfilename": "model-00001-of-00006.safetensors"},
// {"rfilename": "model-00002-of-00006.safetensors"},
// {"rfilename": "model-00003-of-00006.safetensors"},
// {"rfilename": "model-00004-of-00006.safetensors"},
// {"rfilename": "model-00005-of-00006.safetensors"},
// {"rfilename": "model-00006-of-00006.safetensors"},
// {"rfilename": "model.safetensors.index.json"},
// {"rfilename": "recipe.yaml"},
// {"rfilename": "special_tokens_map.json"},
// {"rfilename": "tokenizer.json"},
// {"rfilename": "tokenizer_config.json"}
// ],
// "spaces": ["KBaba7/Quant", "bhaskartripathi/LLM_Quantization", "totolook/Quant", "FallnAI/Quantize-HF-Models", "ruslanmv/convert_to_gguf", "K00B404/LLM_Quantization"],
// "createdAt": "2025-01-30T21:19:42.000Z",
// "safetensors": {
// "parameters": {
// "BF16": 1345868800,
// "F8_E4M3": 22229811200
// },
// "total": 23575680000
// },
// "usedStorage": 24938701317
// }
// ];
// Calculate stats
calculateStats(allModels);
// Initial display
filterModels();
} catch (error) {
console.error('Error fetching models:', error);
loadingElement.innerHTML = `
<div class="text-center">
<i class="fas fa-exclamation-triangle text-red-500 text-4xl mb-3"></i>
<p class="text-red-600">Failed to load models. Please try again later.</p>
</div>
`;
}
}
// Calculate statistics
function calculateStats(models) {
const totalDownloads = models.reduce((sum, model) => sum + (model.downloads || 0), 0);
const totalLikes = models.reduce((sum, model) => sum + (model.likes || 0), 0);
totalDownloadsElement.textContent = totalDownloads.toLocaleString();
totalLikesElement.textContent = totalLikes.toLocaleString();
totalModelsElement.textContent = models.length.toLocaleString();
statsContainer.classList.remove('hidden');
}
// Filter models based on search, filter and sort criteria
function filterModels() {
const searchTerm = searchInput.value.toLowerCase();
const filterValue = filterSelect.value;
const sortValue = sortSelect.value;
filteredModels = allModels.filter(model => {
// Skip private models
if (model.private) return false;
// Search term matching
const matchesSearch = model.id.toLowerCase().includes(searchTerm) ||
(model.cardData && model.cardData.language &&
model.cardData.language.join(' ').toLowerCase().includes(searchTerm)) ||
(model.tags && model.tags.join(' ').toLowerCase().includes(searchTerm));
// Filter matching
const matchesFilter = filterValue === 'all' ||
(model.pipeline_tag && model.pipeline_tag === filterValue) ||
(model.tags && model.tags.includes(filterValue));
return matchesSearch && matchesFilter;
});
// Sort models
if (sortValue === 'downloads') {
filteredModels.sort((a, b) => (b.downloads || 0) - (a.downloads || 0));
} else if (sortValue === 'likes') {
filteredModels.sort((a, b) => (b.likes || 0) - (a.likes || 0));
} else if (sortValue === 'recent') {
filteredModels.sort((a, b) => new Date(b.createdAt || b.lastModified || 0) - new Date(a.createdAt || a.lastModified || 0));
}
// Reset to first page when filtering
currentPage = 1;
displayPaginatedModels();
}
// Filter by tag
function filterByTag(e) {
const tag = e.target.dataset.tag;
activeTag = tag;
// Update active tag styling
tags.forEach(t => {
if (t.dataset.tag === tag) {
t.classList.add('active');
} else {
t.classList.remove('active');
}
});
if (tag === 'all') {
filterSelect.value = 'all';
searchInput.value = '';
filteredModels = [...allModels];
} else {
filteredModels = allModels.filter(model =>
model.tags && model.tags.some(t => t.includes(tag))
);
}
currentPage = 1;
displayPaginatedModels();
}
// Display paginated models
function displayPaginatedModels() {
loadingElement.classList.add('hidden');
if (filteredModels.length === 0) {
noResultsElement.classList.remove('hidden');
modelsContainer.classList.add('hidden');
return;
}
noResultsElement.classList.add('hidden');
modelsContainer.classList.remove('hidden');
// Calculate pagination
const totalPages = Math.ceil(filteredModels.length / modelsPerPage);
const startIndex = (currentPage - 1) * modelsPerPage;
const endIndex = Math.min(startIndex + modelsPerPage, filteredModels.length);
const paginatedModels = filteredModels.slice(startIndex, endIndex);
// Update pagination controls
pageInfoElement.textContent = `Page ${currentPage} of ${totalPages}`;
prevPageButton.disabled = currentPage === 1;
nextPageButton.disabled = currentPage === totalPages;
// Clear and repopulate models container
modelsContainer.innerHTML = '';
paginatedModels.forEach(model => {
const modelCard = document.createElement('div');
modelCard.className = 'model-card bg-white rounded-lg shadow-md overflow-hidden transition-all duration-200 ease-in-out';
// Determine icon based on model type
let icon, iconColor;
if (model.pipeline_tag === 'text-generation' || model.tags?.includes('text-generation')) {
icon = 'fa-comment-alt';
iconColor = 'text-blue-500';
} else if (model.pipeline_tag === 'image-text-to-text' || model.tags?.includes('vision')) {
icon = 'fa-image';
iconColor = 'text-green-500';
} else if (model.pipeline_tag === 'conversational' || model.tags?.includes('conversational')) {
icon = 'fa-comments';
iconColor = 'text-purple-500';
} else {
icon = 'fa-cube';
iconColor = 'text-gray-500';
}
// Format date
const date = model.createdAt || model.lastModified;
const formattedDate = date ? new Date(date).toLocaleDateString() : 'Unknown';
// Create model card
modelCard.innerHTML = `
<div class="p-6">
<div class="flex items-start">
<div class="flex-shrink-0">
<i class="fas ${icon} ${iconColor} text-3xl"></i>
</div>
<div class="ml-4">
<h3 class="text-lg font-medium text-gray-900">${model.id.split('/')[1]}</h3>
<p class="mt-1 text-sm text-gray-500">${model.id}</p>
</div>
</div>
${model.cardData?.license ? `<p class="mt-2 text-xs text-gray-500"><span class="font-medium">License:</span> ${model.cardData.license}</p>` : ''}
${model.cardData?.language ? `<p class="mt-1 text-xs text-gray-500"><span class="font-medium">Languages:</span> ${model.cardData.language.join(', ')}</p>` : ''}
<div class="mt-4 flex flex-wrap gap-2">
${model.tags ? model.tags.slice(0, 5).map(tag => `<span class="px-2 py-1 rounded-full text-xs font-medium bg-gray-100 text-gray-800">${tag}</span>`).join('') : ''}
</div>
</div>
<div class="bg-gray-50 px-6 py-4 border-t border-gray-200">
<div class="flex justify-between items-center">
<div class="flex items-center">
<i class="fas fa-download text-gray-400 mr-1"></i>
<span class="text-xs text-gray-500">${(model.downloads || 0).toLocaleString()}</span>
</div>
<div class="flex items-center">
<i class="fas fa-heart text-gray-400 mr-1"></i>
<span class="text-xs text-gray-500">${(model.likes || 0).toLocaleString()}</span>
</div>
<div class="flex items-center">
<i class="fas fa-calendar-alt text-gray-400 mr-1"></i>
<span class="text-xs text-gray-500">${formattedDate}</span>
</div>
</div>
</div>
<div class="px-6 py-3 bg-gray-50 border-t border-gray-200">
<a href="https://huggingface.co/${model.id}" target="_blank" class="w-full flex items-center justify-center px-4 py-2 border border-transparent text-sm font-medium rounded-md text-white bg-red-600 hover:bg-red-700">
<i class="fab fa-hubspot mr-2"></i> View Model
</a>
</div>
`;
modelsContainer.appendChild(modelCard);
});
}
// Pagination functions
function goToPreviousPage() {
if (currentPage > 1) {
currentPage--;
displayPaginatedModels();
}
}
function goToNextPage() {
const totalPages = Math.ceil(filteredModels.length / modelsPerPage);
if (currentPage < totalPages) {
currentPage++;
displayPaginatedModels();
}
}
// Utility function for debouncing
function debounce(func, wait) {
let timeout;
return function() {
const context = this;
const args = arguments;
clearTimeout(timeout);
timeout = setTimeout(() => {
func.apply(context, args);
}, wait);
};
}
});
</script>
<p style="border-radius: 8px; text-align: center; font-size: 12px; color: #fff; margin-top: 16px;position: fixed; left: 8px; bottom: 8px; z-index: 10; background: rgba(0, 0, 0, 0.8); padding: 4px 8px;">Made with <img src="https://enzostvs-deepsite.hf.space/logo.svg" alt="DeepSite Logo" style="width: 16px; height: 16px; vertical-align: middle;display:inline-block;margin-right:3px;filter:brightness(0) invert(1);"><a href="https://enzostvs-deepsite.hf.space" style="color: #fff;text-decoration: underline;" target="_blank" >DeepSite</a> - 🧬 <a href="https://enzostvs-deepsite.hf.space?remix=mgoin/redhatai-model-explorer" style="color: #fff;text-decoration: underline;" target="_blank" >Remix</a></p></body>
</html> |