{"id":35129,"date":"2026-04-13T14:10:33","date_gmt":"2026-04-13T14:10:33","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/tucano-2-cool-better-open-source-llms-for-portuguese\/"},"modified":"2026-06-08T13:17:36","modified_gmt":"2026-06-08T13:17:36","slug":"tucano-2-cool-better-open-source-llms-for-portuguese","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/tucano-2-cool-better-open-source-llms-for-portuguese\/","title":{"rendered":"Tucano 2 Cool: Better Open Source {LLMs} for Portuguese"},"content":{"rendered":"<p>We present Tucano 2, a fully open suite of large language models ({LLMs}) with 0.5-3.7 billion parameters, designed to address certain gaps in open-source development for Portuguese {LLMs}. Following our previous works, we now extend our dataset, {GigaVerbo}-v2, to a new degree of quality and scale, while also introducing a new synthetic dataset, {GigaVerbo}-v2 Synth, aimed at filling missing gaps in {GigaVerbo}-v2, and two post-training datasets, {GigaVerbo}-v2 {SFT} and {GigaVerbo}-v2 Preferences, that allow Portuguese {LLMs} to be trained in domains like retrieval augmented generation, coding, tool use, chain-of-thought reasoning, and many other domains of interest. Through extensive ablation studies, we design both pretraining and continual pretraining recipes for the Tucano 2 suite (Base, Instruct, and Think), which achieve state-of-the-art performance on several Portuguese-language modeling benchmarks. We also extend and refine the evaluation harness introduced in our earlier work, yielding a comprehensive evaluation suite that provides strong signals across different pretraining, continual pretraining, and post-training regimes. All artifacts associated with Tucano 2 are openly released, including training recipes, logs, and source code, ensuring that our work is reproducible, accessible, and extendable by the broader Portuguese {NLP} community.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present Tucano 2, a fully open suite of large language models ({LLMs}) with 0.5-3.7 billion parameters, designed to address certain gaps in open-source development for Portuguese {LLMs}. Following our previous works, we now extend our dataset, {GigaVerbo}-v2, to a new degree of quality and scale, while also introducing a new synthetic dataset, {GigaVerbo}-v2 Synth, aimed at filling missing gaps in {GigaVerbo}-v2, and two post-training datasets, {GigaVerbo}-v2 {SFT} and {GigaVerbo}-v2 [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[30],"class_list":["post-35129","publication","type-publication","status-publish","hentry","publication-type-article"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/35129","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/35129\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=35129"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=35129"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}