{ // 获取包含Hugging Face文本的span元素 const spans = link.querySelectorAll('span.whitespace-nowrap, span.hidden.whitespace-nowrap'); spans.forEach(span => { if (span.textContent && span.textContent.trim().match(/Hugging\s*Face/i)) { span.textContent = 'AI快站'; } }); }); // 替换logo图片的alt属性 document.querySelectorAll('img[alt*="Hugging"], img[alt*="Face"]').forEach(img => { if (img.alt.match(/Hugging\s*Face/i)) { img.alt = 'AI快站 logo'; } }); } // 替换导航栏中的链接 function replaceNavigationLinks() { // 已替换标记,防止重复运行 if (window._navLinksReplaced) { return; } // 已经替换过的链接集合,防止重复替换 const replacedLinks = new Set(); // 只在导航栏区域查找和替换链接 const headerArea = document.querySelector('header') || document.querySelector('nav'); if (!headerArea) { return; } // 在导航区域内查找链接 const navLinks = headerArea.querySelectorAll('a'); navLinks.forEach(link => { // 如果已经替换过,跳过 if (replacedLinks.has(link)) return; const linkText = link.textContent.trim(); const linkHref = link.getAttribute('href') || ''; // 替换Spaces链接 - 仅替换一次 if ( (linkHref.includes('/spaces') || linkHref === '/spaces' || linkText === 'Spaces' || linkText.match(/^s*Spacess*$/i)) && linkText !== 'OCR模型免费转Markdown' && linkText !== 'OCR模型免费转Markdown' ) { link.textContent = 'OCR模型免费转Markdown'; link.href = 'https://fast360.xyz'; link.setAttribute('target', '_blank'); link.setAttribute('rel', 'noopener noreferrer'); replacedLinks.add(link); } // 删除Posts链接 else if ( (linkHref.includes('/posts') || linkHref === '/posts' || linkText === 'Posts' || linkText.match(/^s*Postss*$/i)) ) { if (link.parentNode) { link.parentNode.removeChild(link); } replacedLinks.add(link); } // 替换Docs链接 - 仅替换一次 else if ( (linkHref.includes('/docs') || linkHref === '/docs' || linkText === 'Docs' || linkText.match(/^s*Docss*$/i)) && linkText !== '模型下载攻略' ) { link.textContent = '模型下载攻略'; link.href = '/'; replacedLinks.add(link); } // 删除Enterprise链接 else if ( (linkHref.includes('/enterprise') || linkHref === '/enterprise' || linkText === 'Enterprise' || linkText.match(/^s*Enterprises*$/i)) ) { if (link.parentNode) { link.parentNode.removeChild(link); } replacedLinks.add(link); } }); // 查找可能嵌套的Spaces和Posts文本 const textNodes = []; function findTextNodes(element) { if (element.nodeType === Node.TEXT_NODE) { const text = element.textContent.trim(); if (text === 'Spaces' || text === 'Posts' || text === 'Enterprise') { textNodes.push(element); } } else { for (const child of element.childNodes) { findTextNodes(child); } } } // 只在导航区域内查找文本节点 findTextNodes(headerArea); // 替换找到的文本节点 textNodes.forEach(node => { const text = node.textContent.trim(); if (text === 'Spaces') { node.textContent = node.textContent.replace(/Spaces/g, 'OCR模型免费转Markdown'); } else if (text === 'Posts') { // 删除Posts文本节点 if (node.parentNode) { node.parentNode.removeChild(node); } } else if (text === 'Enterprise') { // 删除Enterprise文本节点 if (node.parentNode) { node.parentNode.removeChild(node); } } }); // 标记已替换完成 window._navLinksReplaced = true; } // 替换代码区域中的域名 function replaceCodeDomains() { // 特别处理span.hljs-string和span.njs-string元素 document.querySelectorAll('span.hljs-string, span.njs-string, span[class*="hljs-string"], span[class*="njs-string"]').forEach(span => { if (span.textContent && span.textContent.includes('huggingface.co')) { span.textContent = span.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 替换hljs-string类的span中的域名(移除多余的转义符号) document.querySelectorAll('span.hljs-string, span[class*="hljs-string"]').forEach(span => { if (span.textContent && span.textContent.includes('huggingface.co')) { span.textContent = span.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 替换pre和code标签中包含git clone命令的域名 document.querySelectorAll('pre, code').forEach(element => { if (element.textContent && element.textContent.includes('git clone')) { const text = element.innerHTML; if (text.includes('huggingface.co')) { element.innerHTML = text.replace(/huggingface.co/g, 'aifasthub.com'); } } }); // 处理特定的命令行示例 document.querySelectorAll('pre, code').forEach(element => { const text = element.innerHTML; if (text.includes('huggingface.co')) { // 针对git clone命令的专门处理 if (text.includes('git clone') || text.includes('GIT_LFS_SKIP_SMUDGE=1')) { element.innerHTML = text.replace(/huggingface.co/g, 'aifasthub.com'); } } }); // 特别处理模型下载页面上的代码片段 document.querySelectorAll('.flex.border-t, .svelte_hydrator, .inline-block').forEach(container => { const content = container.innerHTML; if (content && content.includes('huggingface.co')) { container.innerHTML = content.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 特别处理模型仓库克隆对话框中的代码片段 try { // 查找包含"Clone this model repository"标题的对话框 const cloneDialog = document.querySelector('.svelte_hydration_boundary, [data-target="MainHeader"]'); if (cloneDialog) { // 查找对话框中所有的代码片段和命令示例 const codeElements = cloneDialog.querySelectorAll('pre, code, span'); codeElements.forEach(element => { if (element.textContent && element.textContent.includes('huggingface.co')) { if (element.innerHTML.includes('huggingface.co')) { element.innerHTML = element.innerHTML.replace(/huggingface.co/g, 'aifasthub.com'); } else { element.textContent = element.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } } }); } // 更精确地定位克隆命令中的域名 document.querySelectorAll('[data-target]').forEach(container => { const codeBlocks = container.querySelectorAll('pre, code, span.hljs-string'); codeBlocks.forEach(block => { if (block.textContent && block.textContent.includes('huggingface.co')) { if (block.innerHTML.includes('huggingface.co')) { block.innerHTML = block.innerHTML.replace(/huggingface.co/g, 'aifasthub.com'); } else { block.textContent = block.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } } }); }); } catch (e) { // 错误处理但不打印日志 } } // 当DOM加载完成后执行替换 if (document.readyState === 'loading') { document.addEventListener('DOMContentLoaded', () => { replaceHeaderBranding(); replaceNavigationLinks(); replaceCodeDomains(); // 只在必要时执行替换 - 3秒后再次检查 setTimeout(() => { if (!window._navLinksReplaced) { console.log('[Client] 3秒后重新检查导航链接'); replaceNavigationLinks(); } }, 3000); }); } else { replaceHeaderBranding(); replaceNavigationLinks(); replaceCodeDomains(); // 只在必要时执行替换 - 3秒后再次检查 setTimeout(() => { if (!window._navLinksReplaced) { console.log('[Client] 3秒后重新检查导航链接'); replaceNavigationLinks(); } }, 3000); } // 增加一个MutationObserver来处理可能的动态元素加载 const observer = new MutationObserver(mutations => { // 检查是否导航区域有变化 const hasNavChanges = mutations.some(mutation => { // 检查是否存在header或nav元素变化 return Array.from(mutation.addedNodes).some(node => { if (node.nodeType === Node.ELEMENT_NODE) { // 检查是否是导航元素或其子元素 if (node.tagName === 'HEADER' || node.tagName === 'NAV' || node.querySelector('header, nav')) { return true; } // 检查是否在导航元素内部 let parent = node.parentElement; while (parent) { if (parent.tagName === 'HEADER' || parent.tagName === 'NAV') { return true; } parent = parent.parentElement; } } return false; }); }); // 只在导航区域有变化时执行替换 if (hasNavChanges) { // 重置替换状态,允许再次替换 window._navLinksReplaced = false; replaceHeaderBranding(); replaceNavigationLinks(); } }); // 开始观察document.body的变化,包括子节点 if (document.body) { observer.observe(document.body, { childList: true, subtree: true }); } else { document.addEventListener('DOMContentLoaded', () => { observer.observe(document.body, { childList: true, subtree: true }); }); } })(); \n\n\n","html":"\n\n

Explore Arm-Optimized Learning Paths on Hugging Face

\n

Arm’s AI development resources help you deploy faster with optimized performance on Arm by default. The resources highlight Arm integrations with major OSes and frameworks to simplify development and enable scalability and portability for your applications.

\n

Follow our curated Learning Paths to explore Arm-optimized AI models available in our Hugging Face Model Collections, and to streamline your journey from discovery to deployment on Arm.

\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n \n \n \n\n\n\n \n \n \n\n\n \n \n \n\n \n \n \n \n\n\n\n \n \n \n\n\n \n \n \n\n\n
Learning PathMarket ApplicationModel(s) Featured
Generative AI
Build a RAG application using Zilliz Cloud on Arm serversCloud & DatacenterAll Minilm L6 V2
Accelerate NLP models from Hugging Face on Arm serversCloud & DatacenterDistilbert Base Uncased Finetuned Sst 2 English
Deploy a Large Language Model (LLM) chatbot with llama.cpp using KleidiAI on Arm serversCloud & DatacenterDolphin 2.9.4 Llama3.1 8B Gguf
Build a RAG application using Zilliz Cloud on Arm serversCloud & DatacenterDolphin 2.9.4 Llama3.1 8B Gguf
Run a Large Language Model (LLM) chatbot with PyTorch using KleidiAI on Arm serversCloud & DatacenterLlama 3.1 8B Instruct
Deploy a RAG-based Chatbot with llama-cpp-python using KleidiAI on Google Axion processorsCloud & DatacenterLlama3.1 8B Gguf
Build an Android chat app with Llama, KleidiAI, ExecuTorch, and XNNPACKSmartphoneLlama 3.2 1B Instruct
Run a local LLM chatbot on a Raspberry Pi 5Raspberry PiOrca Mini 3B Gguf
Build an Android chat application with ONNX Runtime APISmartphonePhi 3 Vision 128K Instruct Onnx Cuda
Run an LLM chatbot with rtp-llm on Arm-based serversCloud & DatacenterQwen2 0.5B Instruct
Build and Run a Virtual Large Language Model (vLLM) on Arm ServersCloud & DatacenterQwen2.5 0.5B Instruct
LLM inference on Android with KleidiAI, MediaPipe, and XNNPACKSmartphoneGemma 2B
Run Llama 3 on a Raspberry Pi 5 using ExecuTorchRaspberry PiLlama 3.1 8B
CV: Image Classification & Object Detection
Profile the Performance of AI and ML Mobile Applications on ArmSmartphoneMobilenet V2 1.0 224
Run a Computer Vision Model on a Himax MicrocontrollerIoTYolov8
Export a simple PyTorch model (e.g. MobileNetV2) for ExecuTorch Arm Ethos-UIoTDistilbert Base Uncased Finetuned Sst 2 English
Sentiment Analysis
Run a Natural Language Processing (NLP) model from Hugging Face on Arm serversCloud & DatacenterTwitter Roberta Base Sentiment Latest
Accelerate NLP models from Hugging Face on Arm serversCloud & DatacenterDistilbert Base Uncased Finetuned Sst 2 English
\n\n
\nArm Kleidi: Unleashing Mass-Market AI Performance on Arm\n

Arm Kleidi developer enablement technologies and kernel libraries unlock AI performance across the world's popular AI frameworks and runtimes, accelerating AI workloads everywhere on Arm. Ecosystem integrations help application developers achieve top performance by default and future-proof performance, with no additional work or skills investment.

\n

Useful Resources on Arm Kleidi:

\n\n\n

Note: The data collated here is sourced from Arm and third parties. While Arm uses reasonable efforts to keep this information accurate, Arm does not warrant (express or implied) or provide any guarantee of data correctness due to the ever-evolving AI and software landscape. Any links to third-party sites and resources are provided for ease and convenience. Your use of such third-party sites and resources is subject to the third party’s terms of use, and use is at your own risk.

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