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| <link rel="modulepreload" href="/docs/course/pr_1213/es/_app/immutable/chunks/CourseFloatingBanner.f0a2dc21.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"Dominar los LLMs","local":"mastering-llms","sections":[{"title":"Del PLN clásico a los LLMs","local":"del-pln-clásico-a-los-llms","sections":[],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <div class="items-center shrink-0 min-w-[100px] max-sm:min-w-[50px] justify-end ml-auto flex" style="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"><div class="inline-flex rounded-md max-sm:rounded-sm"><button class="inline-flex items-center gap-1 h-7 max-sm:h-7 px-2 max-sm:px-1.5 text-sm font-medium text-gray-800 border border-r-0 rounded-l-md max-sm:rounded-l-sm border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-live="polite"><span class="inline-flex items-center 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0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Dominar los LLMs</span></h1> <div class="flex space-x-1 absolute z-10 right-0 top-0" style=""><a href="https://discuss.huggingface.co/t/chapter-7-questions" target="_blank"><img alt="Ask a Question" class="!m-0" 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</div> <p data-svelte-h="svelte-1e68396">Si llegaste hasta aquí en el curso, ya tienes la base técnica necesaria para trabajar con casi cualquier tarea de lenguaje usando 🤗 Transformers y el ecosistema de Hugging Face.</p> <h2 class="relative group"><a id="del-pln-clásico-a-los-llms" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#del-pln-clásico-a-los-llms"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Del PLN clásico a los LLMs</span></h2> <p data-svelte-h="svelte-4yno1b">Aunque en este curso cubrimos tareas clásicas de PLN, el campo ha cambiado mucho con los modelos de lenguaje de gran tamaño (LLMs). Estos modelos han ampliado de forma drástica lo que puede hacerse con lenguaje natural:</p> <ul data-svelte-h="svelte-1tofa33"><li>Pueden resolver varias tareas sin ajuste fino específico para cada una.</li> <li>Destacan siguiendo instrucciones y adaptándose al contexto.</li> <li>Generan texto coherente en una gran variedad de aplicaciones.</li> <li>Permiten nuevas técnicas de prompting y razonamiento guiado.</li></ul> <p data-svelte-h="svelte-1f1hevr">Las bases que aprendiste aquí siguen siendo centrales. Entender tokenización, arquitecturas, ajuste fino, evaluación y despliegue sigue siendo exactamente lo que permite trabajar con LLMs con criterio y no como una caja negra.</p> <iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/-RPeakdlHYo" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> <p data-svelte-h="svelte-1brjew3">Después de esta parte del curso deberías poder:</p> <ul data-svelte-h="svelte-1suoh8w"><li>Elegir la arquitectura adecuada para una tarea.</li> <li>Entender la diferencia entre preentrenamiento y ajuste fino.</li> <li>Entrenar modelos con <code>Trainer</code>, Accelerate o TensorFlow/Keras.</li> <li>Evaluar tareas generativas con métricas como BLEU y ROUGE.</li> <li>Publicar y reutilizar modelos desde el Hub.</li></ul> <p data-svelte-h="svelte-18m2t4w">El siguiente paso natural es aprender a depurar mejor tus pipelines, comunicar errores con precisión y apoyarte en la comunidad cuando aparezcan problemas complejos.</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/course/blob/main/chapters/es/chapter7/8.mdx" target="_blank"><svg class="mr-1" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M31,16l-7,7l-1.41-1.41L28.17,16l-5.58-5.59L24,9l7,7z"></path><path d="M1,16l7-7l1.41,1.41L3.83,16l5.58,5.59L8,23l-7-7z"></path><path d="M12.419,25.484L17.639,6.552l1.932,0.518L14.351,26.002z"></path></svg> <span data-svelte-h="svelte-zjs2n5"><span class="underline">Update</span> on GitHub</span></a> <p></p> | |
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