{"id":194983,"date":"2025-05-02T06:06:00","date_gmt":"2025-05-02T05:06:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=194983"},"modified":"2025-05-09T16:07:33","modified_gmt":"2025-05-09T15:07:33","slug":"all-about-microsoft-phi-4","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/all-about-microsoft-phi-4","title":{"rendered":"Microsoft Phi-4: What are the particularities of this AI model?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"194983\" class=\"elementor elementor-194983\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a342c37 e-flex e-con-boxed e-con\" data-id=\"a342c37\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-dd081cf elementor-widget elementor-widget-text-editor\" data-id=\"dd081cf\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><b>Phi-4 is the newest language model developed by Microsoft, pushing the boundaries of small language models with no fewer than 14 billion parameters. It\u2019s designed for top-notch performance in complex reasoning, especially in mathematics, while excelling in classic language processing.<\/b><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-b8680ac e-flex e-con-boxed e-con\" data-id=\"b8680ac\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b9beb2a elementor-widget elementor-widget-heading\" data-id=\"b9beb2a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-xl\">What is Phi-4 exactly?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3084b98 elementor-widget elementor-widget-text-editor\" data-id=\"3084b98\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Phi-4 is a <\/span><b>small language model (SLM)<\/b><span style=\"font-weight: 400;\"> created by <\/span><b>Microsoft<\/b><span style=\"font-weight: 400;\">, with 14 billion parameters. Originally available on <\/span><b>Azure AI Foundry<\/b><span style=\"font-weight: 400;\">, it is now accessible as open source on Hugging Face under the <\/span><b>MIT license<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">This model stands out for its superior performance compared to Google Gemini<\/span><b> Pro 1.5<\/b><span style=\"font-weight: 400;\"> and OpenAI GPT-4, particularly in complex tasks like <\/span><b>mathematical reasoning<\/b><span style=\"font-weight: 400;\">, while consuming fewer computing resources than <\/span><a href=\"https:\/\/datascientest.com\/en\/large-language-models-llm-everything-you-need-to-know\"><span style=\"font-weight: 400;\">large language models<\/span><\/a><span style=\"font-weight: 400;\"> (LLM).<\/span><\/p><p><span style=\"font-weight: 400;\">Phi-4 is designed using a mix of <\/span><b>synthetic data<\/b><span style=\"font-weight: 400;\">, public domain websites, academic literature, and question-answer datasets. It has been optimized to deliver high-quality results with advanced reasoning. Microsoft has focused on the model&#8217;s robustness and security, using <\/span><b>supervised fine-tuning<\/b><span style=\"font-weight: 400;\"> (SFT) techniques and <\/span><b>direct preference optimization<\/b><span style=\"font-weight: 400;\"> (DPO) to ensure precise adherence to instructions and solid security measures.<\/span><\/p><p><b>Phi-4<\/b><span style=\"font-weight: 400;\"> is particularly suited to environments with memory and compute constraints, as well as scenarios requiring low latency. Overall, it represents a significant advancement in language model research, offering a powerful and resource-efficient alternative for <\/span><b>generative AI applications<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-5a7e543 e-flex e-con-boxed e-con\" data-id=\"5a7e543\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-08c2670 elementor-widget elementor-widget-heading\" data-id=\"08c2670\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">How does Phi-4 perform?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-88919a7 elementor-widget elementor-widget-text-editor\" data-id=\"88919a7\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">In a landscape where the power of <\/span><b>language models<\/b><span style=\"font-weight: 400;\"> is often associated with their size, <\/span><b>Phi-4<\/b><span style=\"font-weight: 400;\"> challenges this trend by proving that a compact model can rival much larger architectures. Thanks to extensive optimization, it achieves a high level of performance in understanding and reasoning while maintaining a reduced footprint.<\/span><\/p><p><span style=\"font-weight: 400;\">Where other models require tens of billions of active parameters to deliver comparable results, Phi-4 emerges as <\/span><b>a perfect balance between efficiency and power<\/b><span style=\"font-weight: 400;\">. This strategic positioning meets a growing demand for more accessible and resource-efficient AI, without compromising quality.<\/span><\/p><p><span style=\"font-weight: 400;\">Its optimized architecture allows not only faster execution but also better adaptability to resource-limited environments, such as <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-embedded-systems\"><span style=\"font-weight: 400;\">embedded applications<\/span><\/a><span style=\"font-weight: 400;\"> or low-energy servers. By reducing dependency on massive infrastructures, Phi-4 paves the way for the democratization of <\/span><a href=\"https:\/\/datascientest.com\/en\/artificial-intelligence-definition\"><span style=\"font-weight: 400;\">artificial intelligence<\/span><\/a><span style=\"font-weight: 400;\">, where high performance and energy efficiency are no longer incompatible. It illustrates a new generation of models capable of meeting industrial and academic needs while remaining agile and scalable.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3bb1cf4 elementor-widget elementor-widget-image\" data-id=\"3bb1cf4\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1920\" height=\"1080\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/04\/Frame-6-1.webp\" class=\"attachment-full size-full wp-image-194986\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/04\/Frame-6-1.webp 1920w, https:\/\/datascientest.com\/en\/files\/2025\/04\/Frame-6-1-300x169.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/04\/Frame-6-1-1024x576.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/04\/Frame-6-1-768x432.webp 768w, https:\/\/datascientest.com\/en\/files\/2025\/04\/Frame-6-1-1536x864.webp 1536w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Source : <a href=\"https:\/\/techcommunity.microsoft.com\/blog\/aiplatformblog\/introducing-phi-4-microsoft%E2%80%99s-newest-small-language-model-specializing-in-comple\/4357090\">microsoft.com<\/a><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f77c933 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"f77c933\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/datascientest.com\/en\/deep-learning-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Learn to develop AI<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-b6454f0 e-flex e-con-boxed e-con\" data-id=\"b6454f0\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2df9b1b elementor-widget elementor-widget-heading\" data-id=\"2df9b1b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">What distinguishes Phi-4 from similar models?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4e6b39a elementor-widget elementor-widget-text-editor\" data-id=\"4e6b39a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Phi-4 stands out with its excellent optimization, competing with slightly larger models (like Qwen2.5-14B or Mixtral) while remaining lightweight and efficient. Its <\/span><b>MMLU<\/b><span style=\"font-weight: 400;\"> score of 85 makes it a very competitive model in the <\/span><b>SLM (Small Language Models)<\/b><span style=\"font-weight: 400;\"> category.<\/span><\/p><table><tbody><tr><td><p><b>Model<\/b><\/p><\/td><td><p><b>Active Parameters (B)<\/b><\/p><\/td><td><p><b>MMLU Score<\/b><\/p><\/td><td><p><b>Type<\/b><\/p><\/td><td><p><b>Main Advantages<\/b><\/p><\/td><td><p><b>Disadvantages<\/b><\/p><\/td><\/tr><tr><td><p><b>Phi-4<\/b><\/p><\/td><td><p><span style=\"font-weight: 400;\">~10B<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">~85<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Optimized, compact model<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Excellent performance for its size, efficient in inference, good reasoning<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Less powerful than larger models like GPT-4 or Llama 3-70B<\/span><\/p><\/td><\/tr><tr><td><p><span style=\"font-weight: 400;\">Mixtral (<\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-mistral-ai\"><span style=\"font-weight: 400;\">Mistral AI<\/span><\/a><span style=\"font-weight: 400;\">)<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">12.9B (MoE, 2 active experts)<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">~82-83<\/span><\/p><\/td><td><p><b>MoE (Mixture of Experts)<\/b><\/p><\/td><td><p><span style=\"font-weight: 400;\">Very good balance between performance and efficiency, fast and optimized<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Heavier in inference than Phi-4<\/span><\/p><\/td><\/tr><tr><td><p><b>Qwen2.5-14B-Instruct<\/b><\/p><\/td><td><p><span style=\"font-weight: 400;\">14B<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">~80<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Dense model<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Good understanding of <\/span><a href=\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\"><span style=\"font-weight: 400;\">natural language<\/span><\/a><span style=\"font-weight: 400;\">, strong in general tasks<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Less optimized than Phi-4, requires more power<\/span><\/p><\/td><\/tr><tr><td><p><a href=\"https:\/\/datascientest.com\/en\/all-about-llama\"><span style=\"font-weight: 400;\">Llama<\/span><\/a><span style=\"font-weight: 400;\"> 3.1-8B-Instruct<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">8B<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">~70<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Dense model<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Lightweight and efficient, good compromise for certain tasks<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Inferior in overall performance to Phi-4<\/span><\/p><\/td><\/tr><tr><td><p><b>Mistral-8B-Instruct<\/b><\/p><\/td><td><p><span style=\"font-weight: 400;\">8B<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">~68-70<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Dense model<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Very efficient in inference, <\/span><b>open-source<\/b><\/p><\/td><td><p><span style=\"font-weight: 400;\">Lower MMLU score than Phi-4, less versatile<\/span><\/p><\/td><\/tr><tr><td><p><b>Granite-3-8B-Instruct<\/b><\/p><\/td><td><p><span style=\"font-weight: 400;\">8B<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">~65-67<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Dense model<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Compact and fast<\/span><\/p><\/td><td><p><span style=\"font-weight: 400;\">Less performant than Phi-4 in reasoning and analysis<\/span><\/p><\/td><\/tr><\/tbody><\/table><p><span style=\"font-weight: 400;\">Where other models require more parameters to achieve similar performance, Phi-4 leverages an advanced architecture and specific optimizations to maximize its efficiency. This allows it not only to outperform comparably-sized models but also to approach the capabilities of larger models while maintaining a reduced footprint.<\/span><\/p><p><span style=\"font-weight: 400;\">Its fast inference and balance between power and resource consumption make it ideal for various use cases, particularly in professional and academic settings. Unlike heavier models that require robust infrastructures to operate optimally, Phi-4 positions itself as an agile solution, perfectly suited to systems constrained by computing power. By integrating advanced <\/span><a href=\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\"><span style=\"font-weight: 400;\">language processing<\/span><\/a><span style=\"font-weight: 400;\"> and reasoning mechanisms, it stands out as a strategic choice for those seeking a high-performing, accessible, and efficient AI.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6248d6e elementor-widget elementor-widget-image\" data-id=\"6248d6e\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"448\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/04\/dst_acquisition_An_AI_brain_solving_complex_logical_puzzles_int_e055e2b7-28b7-4737-bfd4-1424a2681c6f-1024x574.webp\" class=\"attachment-large size-large wp-image-194987\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/04\/dst_acquisition_An_AI_brain_solving_complex_logical_puzzles_int_e055e2b7-28b7-4737-bfd4-1424a2681c6f-1024x574.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/04\/dst_acquisition_An_AI_brain_solving_complex_logical_puzzles_int_e055e2b7-28b7-4737-bfd4-1424a2681c6f-300x168.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/04\/dst_acquisition_An_AI_brain_solving_complex_logical_puzzles_int_e055e2b7-28b7-4737-bfd4-1424a2681c6f-768x430.webp 768w, https:\/\/datascientest.com\/en\/files\/2025\/04\/dst_acquisition_An_AI_brain_solving_complex_logical_puzzles_int_e055e2b7-28b7-4737-bfd4-1424a2681c6f.webp 1456w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-126af04 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"126af04\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Mastering language models<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0808c45 e-flex e-con-boxed e-con\" data-id=\"0808c45\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c4325a5 elementor-widget elementor-widget-heading\" data-id=\"c4325a5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">Use Case: Phi-4 addressing a mathematical logic problem<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8786925 elementor-widget elementor-widget-text-editor\" data-id=\"8786925\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Imagine a student preparing for a mathematics exam and tackling a complex problem:<\/span><\/p><p><span style=\"font-weight: 400;\">&#8220;A snail climbs a 10-meter wall. Each day, it ascends 3 meters but slides down 2 meters at night. How many days will it take to reach the top?&#8221;<\/span><\/p><p><span style=\"font-weight: 400;\">A classic model might simply provide an answer by mechanically calculating:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Day 1: It climbs to 3 m, slides down to 1 m<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Day 2: It climbs to 4 m, slides down to 2 m<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">And so on.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cc9f243 elementor-widget elementor-widget-text-editor\" data-id=\"cc9f243\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">But Phi-4 goes further by applying structured reasoning:<\/span><\/p><ol><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">It identifies the recurring pattern: each day, the snail effectively progresses by a net of 1 meter.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">It optimizes reasoning: after 7 days, it reaches 7 meters.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">It detects an exception: on the eighth day, it climbs directly 3 meters and reaches the top without sliding back.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Hence, it correctly concludes that it takes 8 days for the snail to reach the top.<\/span><\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ff55412 elementor-widget elementor-widget-text-editor\" data-id=\"ff55412\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">This type of structured reasoning shows how Phi-4 goes beyond automatic responses: it breaks down a problem, identifies hidden challenges, and arrives at a solution in a logical and efficient manner.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-cd82e52 e-flex e-con-boxed e-con\" data-id=\"cd82e52\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a2b6dc4 elementor-widget elementor-widget-heading\" data-id=\"a2b6dc4\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">Conclusion<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4dbba1c elementor-widget elementor-widget-text-editor\" data-id=\"4dbba1c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">In conclusion, Phi-4 represents a significant advancement in the field of small language models (SLM), combining performance, efficiency, and accessibility. This model positions Microsoft at the forefront of innovation in generative AI, offering a powerful and versatile solution for varied applications, particularly in complex reasoning and language processing.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-59f7fdd elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"59f7fdd\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Become an expert in AI<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-9c96ab5 e-flex e-con-boxed e-con\" data-id=\"9c96ab5\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ec595ee elementor-widget elementor-widget-html\" data-id=\"ec595ee\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"What is Microsoft Phi-4?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Microsoft Phi-4 is an advanced artificial intelligence model developed by Microsoft. 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Phi-4 is a small language model (SLM) created by Microsoft, with [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":194985,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2427],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Microsoft Phi-4: What are the particularities of this AI model?<\/title>\n<meta name=\"description\" content=\"Phi-4 is the latest language model developed by Microsoft, pushing the boundaries of small language models with no less than 14 billion parameters. 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