{"id":2760,"date":"2023-10-31T17:00:08","date_gmt":"2023-10-31T17:00:08","guid":{"rendered":"https:\/\/tekmonk.edu.vn\/ai\/ggml\/"},"modified":"2023-10-31T17:00:08","modified_gmt":"2023-10-31T17:00:08","slug":"ggml","status":"publish","type":"post","link":"https:\/\/tekmonk.edu.vn\/ai\/ggml\/","title":{"rendered":"GGML"},"content":{"rendered":"<p><b>What is GGML?<\/b><\/p>\n<div class=\"MuiGrid-root MuiGrid-item MuiGrid-grid-xs-12 MuiGrid-grid-sm-12 MuiGrid-grid-md-7 MuiGrid-grid-lg-7 css-1ak9ift\">\n<p class=\"MuiTypography-root MuiTypography-body1 css-3yew40\">GGML is a tensor library for machine learning to enable large models and high performance on commodity hardware.<\/p>\n<div class=\"css-6gd8i7\">\n<div><\/div>\n<p class=\"MuiTypography-root MuiTypography-body1 css-9l3uo3\">Added on<!-- --> <!-- -->June 10<!-- -->, <!-- -->2023<\/p>\n<\/div>\n<div class=\"MuiBox-root css-1yjvs5a\">\n<div class=\"MuiBox-root css-1c5hyg8\">\n<div class=\"MuiGrid-root MuiGrid-container css-1kf4fa7\">\n<div class=\"MuiGrid-root MuiGrid-item MuiGrid-grid-xs-9 MuiGrid-grid-sm-9 MuiGrid-grid-md-9 MuiGrid-grid-lg-9 css-1ui2luj\">\n<div class=\"MuiChip-root MuiChip-outlined MuiChip-sizeMedium MuiChip-colorDefault MuiChip-outlinedDefault css-1e478s8\"><span class=\"MuiChip-label MuiChip-labelMedium css-9iedg7\">Free<\/span><\/div>\n<\/div>\n<\/div>\n<div class=\"MuiGrid-root MuiGrid-container css-1kf4fa7\">\n<div class=\"MuiGrid-root MuiGrid-item MuiGrid-grid-xs-9 MuiGrid-grid-sm-9 MuiGrid-grid-md-9 MuiGrid-grid-lg-9 css-1ui2luj\">\n<div class=\"MuiChip-root MuiChip-outlined MuiChip-sizeMedium MuiChip-colorDefault MuiChip-outlinedDefault css-1e478s8\"><span class=\"MuiChip-label MuiChip-labelMedium css-9iedg7\">Open Source<\/span><\/div>\n<\/div>\n<\/div>\n<p class=\"MuiTypography-root MuiTypography-body2 css-yxn4vr\">Social Links<\/p>\n<div class=\"css-1t62lt9\"><a href=\"https:\/\/github.com\/ggerganov\/ggml\" target=\"_blank\" rel=\"noopener nofollow\"><button class=\"MuiButtonBase-root MuiIconButton-root MuiIconButton-sizeMedium css-13a1fpe\" type=\"button\"><\/button><\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2>GGML Features<\/h2>\n<div class=\"MuiBox-root css-0\">\n<div>\n<p class=\"MuiTypography-root MuiTypography-body1 css-10qcplv\"><strong>GGML <\/strong>(Generic Graph Machine Learning) is a powerful tensor library that caters to the needs of machine learning practitioners. It <em>provides a robust set of features and optimizations that enable the training <\/em>of large-scale models and high-performance computing on commodity hardware.<\/p>\n<p class=\"MuiTypography-root MuiTypography-body1 css-10qcplv\"><strong>Key Features:<\/strong><\/p>\n<ul class=\"MuiList-root MuiList-padding css-196imzh\">\n<li class=\"MuiListItem-root MuiListItem-gutters MuiListItem-padding css-8k4qb7\">\n<div class=\"MuiListItemText-root css-1tsvksn\"><span class=\"MuiTypography-root MuiTypography-body1 MuiListItemText-primary css-yb0lig\"><strong>C-based Implementation:<\/strong> GGML is written in C, providing efficiency and compatibility across platforms.<\/span><\/div>\n<\/li>\n<li class=\"MuiListItem-root MuiListItem-gutters MuiListItem-padding css-8k4qb7\">\n<div class=\"MuiListItemText-root css-1tsvksn\"><span class=\"MuiTypography-root MuiTypography-body1 MuiListItemText-primary css-yb0lig\"><strong>16-bit Float Support:<\/strong> Supports 16-bit floating-point operations, reducing memory requirements and improving computation speed.<\/span><\/div>\n<\/li>\n<li class=\"MuiListItem-root MuiListItem-gutters MuiListItem-padding css-8k4qb7\">\n<div class=\"MuiListItemText-root css-1tsvksn\"><span class=\"MuiTypography-root MuiTypography-body1 MuiListItemText-primary css-yb0lig\"><strong>Integer Quantization:<\/strong> Enables optimization of memory and computation by quantizing model weights and activations to lower bit precision.<\/span><\/div>\n<\/li>\n<\/ul>\n<p class=\"MuiTypography-root MuiTypography-body1 css-10qcplv\"><strong>Use Cases:<\/strong><\/p>\n<ul class=\"MuiList-root MuiList-padding css-196imzh\">\n<li class=\"MuiListItem-root MuiListItem-gutters MuiListItem-padding css-8k4qb7\">\n<div class=\"MuiListItemText-root css-1tsvksn\"><span class=\"MuiTypography-root MuiTypography-body1 MuiListItemText-primary css-yb0lig\"><strong>Large-scale Model Training:<\/strong> GGML is ideal for training machine learning models that require extensive computational resources.<\/span><\/div>\n<\/li>\n<li class=\"MuiListItem-root MuiListItem-gutters MuiListItem-padding css-8k4qb7\">\n<div class=\"MuiListItemText-root css-1tsvksn\"><span class=\"MuiTypography-root MuiTypography-body1 MuiListItemText-primary css-yb0lig\"><strong>High-Performance Computing:<\/strong> GGML&#x27;s optimizations make it well-suited for high-performance computing tasks in machine learning.<\/span><\/div>\n<\/li>\n<\/ul>\n<p class=\"MuiTypography-root MuiTypography-body1 css-10qcplv\"><strong>GGML <\/strong>is a powerful tensor library designed to meet the demands of machine learning practitioners.<\/p>\n<\/div>\n<\/div>\n<h2>GGML Website:<\/h2>\n<p><a href=\"https:\/\/ggml.ai\">GGML<\/a><\/p>\n<p>AI Tools Source: <a href=\"https:\/\/tekmonk.edu.vn\/ai\/\">Best Ai Tools<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>What is GGML? GGML is a tensor library for machine learning to enable large models and high performance on commodity hardware. Added on June 10, [&hellip;] <a class=\"g1-link g1-link-more\" href=\"https:\/\/tekmonk.edu.vn\/ai\/ggml\/\">More<\/a><\/p>\n","protected":false},"author":1,"featured_media":2761,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[50],"tags":[],"reaction":[],"adace-sponsor":[],"class_list":{"0":"post-2760","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-code-assistant"},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>GGML<\/title>\n<meta name=\"description\" content=\"What is GGML?GGML is a tensor library for machine learning to enable large models and high performance on commodity hardware.Added on June 10,\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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