{"id":222056,"date":"2026-05-28T17:54:00","date_gmt":"2026-05-28T21:54:00","guid":{"rendered":"https:\/\/testing.news-you-need.com\/index.php\/2026\/05\/28\/training-azerbaijani-language-models-on-amazon-sagemaker-ai\/"},"modified":"2026-05-28T18:15:13","modified_gmt":"2026-05-28T22:15:13","slug":"training-azerbaijani-language-models-on-amazon-sagemaker-ai","status":"publish","type":"post","link":"https:\/\/testing.news-you-need.com\/index.php\/2026\/05\/28\/training-azerbaijani-language-models-on-amazon-sagemaker-ai\/","title":{"rendered":"Training Azerbaijani language models on Amazon SageMaker AI"},"content":{"rendered":"<p><a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/training-azerbaijani-language-models-on-amazon-sagemaker-ai\/\">Training Azerbaijani language models on Amazon SageMaker AI<\/a><\/p>\n<p><a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/training-azerbaijani-language-models-on-amazon-sagemaker-ai\/\">https:\/\/aws.amazon.com\/blogs\/machine-learning\/training-azerbaijani-language-models-on-amazon-sagemaker-ai\/<\/a><\/p>\n<p>Publish Date: <a href=\"publish_date]\">2026-05-28 17:54:00<\/a><\/p>\n<p>Source Domain: <a href=\"aws.amazon.com\">aws.amazon.com<\/a><\/p>\n<ul>\n<li>\n<p><strong>Project Background and Collaboration:<\/strong> The project integrates open-source tools like PyTorch, Hugging Face Transformers, and Liger Kernels, with contributions from Azercell Telecom and the AWS Generative AI Innovation Center.<\/p>\n<\/li>\n<li>\n<p><strong>Framework Development:<\/strong> The framework comprises three main stages: efficient custom tokenizer development, continued pre-training for foundation model adaptation, and supervised fine-tuning with LoRA.<\/p>\n<\/li>\n<li>\n<p><strong>Tokenizer Efficiency:<\/strong> The custom monolingual tokenizer achieved a 2\u00d7 improvement in encoding efficiency, effectively doubling the amount of Azerbaijani text the model can process within its context window.<\/p>\n<\/li>\n<li>\n<p><strong>Memory and Throughput Optimization:<\/strong> The use of Fully Sharded Data Parallel (FSDP) and Liger Kernels allowed for larger batch sizes, 23% higher training throughput, and 58% lower peak GPU memory usage.  <\/p>\n<\/li>\n<li>\n<p><strong>Scalable Infrastructure:<\/strong> The solution provides a scalable and production-ready training framework tailored for growing training requirements and is designed to scale up with minimal changes.<\/p>\n<\/li>\n<li>\n<p><strong>Language Understanding and Generation:<\/strong> The fine-tuned model on Amazon SageMaker AI showed coherent Azerbaijan language generation, contrasting with the incoherent output from the non-fine-tuned foundation model.<\/p>\n<\/li>\n<li>\n<p><strong>Training Pipeline:<\/strong> The framework trains in three distinct stages with each stage optimizing for different aspects, starting from custom tokenizer development to achieving high throughput with memory optimizations and using efficient fine-tuning methods.<\/p>\n<\/li>\n<li>\n<p><strong>Conclusion and Implementation:<\/strong> The success of this model-building framework on Amazon SageMaker AI demonstrates a scalable methodology adaptable for other low-resource languages or scenarios that optimize GPU utilizations, offering a pathway for similar implementations.<\/p>\n<\/li>\n<\/ul>\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Training Azerbaijani language models on Amazon SageMaker AI https:\/\/aws.amazon.com\/blogs\/machine-learning\/training-azerbaijani-language-models-on-amazon-sagemaker-ai\/ Publish Date: 2026-05-28 17:54:00 Source Domain:&#8230;<\/p>\n","protected":false},"author":1,"featured_media":222057,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"https:\/\/d2908q01vomqb2.cloudfront.net\/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59\/2026\/05\/28\/20305.png","fifu_image_alt":"","footnotes":""},"categories":[14],"tags":[19],"class_list":["post-222056","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","tag-generative-ai"],"_links":{"self":[{"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/posts\/222056"}],"collection":[{"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/comments?post=222056"}],"version-history":[{"count":1,"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/posts\/222056\/revisions"}],"predecessor-version":[{"id":222058,"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/posts\/222056\/revisions\/222058"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/media\/222057"}],"wp:attachment":[{"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/media?parent=222056"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/categories?post=222056"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/testing.news-you-need.com\/index.php\/wp-json\/wp\/v2\/tags?post=222056"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}