{"id":245503,"date":"2026-10-16T09:09:35","date_gmt":"2026-10-16T14:09:35","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2026\/10\/smollm-q-how-do-small-language-models-slms-get-trained"},"modified":"2026-10-16T09:09:35","modified_gmt":"2026-10-16T14:09:35","slug":"smollm-q-how-do-small-language-models-slms-get-trained","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2026\/10\/smollm-q-how-do-small-language-models-slms-get-trained","title":{"rendered":"SmolLM: Q: How do Small Language Models (SLMs) get trained?"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/smollm2.jpg\"><\/a><\/p>\n<p>A: Small Language Models are typically built using three main approaches:<\/p>\n<p>1. Training from scratch: The model learns language patterns from large, carefully curated datasets, with its parameters initialized and trained rather than inherited from an existing pre-trained model.<\/p>\n<p>2. Fine-tuning a pre-trained model: An existing model is further trained on selected data to improve its performance on specific tasks, domains, or instructions.<\/p>\n<p>3. Knowledge distillation: A smaller student model learns from the outputs, predictions, or other training signals of a more capable *teacher model*, transferring useful capabilities into a more compact system.<\/p>\n<p>SLMs are often described as models with fewer than 10 billion parameters, although there is no universally agreed-upon size threshold.<\/p>\n<p>These approaches can also be combined to build models that balance capability, computational efficiency, and deployment costs.<\/p>\n<p>References:<\/p>\n<div class=\"more-link-wrapper\"> <a class=\"more-link\" href=\"https:\/\/lifeboat.com\/blog\/2026\/10\/smollm-q-how-do-small-language-models-slms-get-trained\">Continue reading \u201cSmolLM: Q: How do Small Language Models (SLMs) get trained?\u201d | &gt;<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"<p>A: Small Language Models are typically built using three main approaches: 1. Training from scratch: The model learns language patterns from large, carefully curated datasets, with its parameters initialized and trained rather than inherited from an existing pre-trained model. 2. Fine-tuning a pre-trained model: An existing model is further trained on selected data to improve [\u2026]<\/p>\n","protected":false},"author":709,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-245503","post","type-post","status-publish","format-standard","hentry","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/245503","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/709"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=245503"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/245503\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=245503"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=245503"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=245503"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}