Finetuning LLM<\/title><br \/>\n <meta name=\"description\" content=\"Finetuning LLM\"\/>\n <link rel=\"stylesheet\" type=\"text\/css\" href=\"https:\/\/www.easiio.com\/wp-content\/themes\/easiio\/assets\/css\/easiio-new\/common.css\" \/>\n <link rel=\"stylesheet\" type=\"text\/css\" href=\"https:\/\/www.easiio.com\/wp-content\/themes\/easiio\/assets\/css\/easiio-new\/page-index.css\" \/>\n <link rel=\"stylesheet\" type=\"text\/css\" href=\"https:\/\/www.easiio.com\/wp-content\/themes\/easiio\/assets\/css\/mpg-index.css\" \/>\n<style>\n body{\n display:block !important;\n }\n <\/style>\n<div class=\"mpg-index-page\">\n<div class=\"mpg-section-LLM\" style=\"background-image: url(https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/llm-section-1.png); \">\n<div class=\"container\">\n<div class=\"section-content\">\n<div class=\"text\">\n<h1>\n LLM: Unleashing the Power of Large Language Models<br \/>\n <\/h1>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"mpg-section-keyword\">\n<div class=\"container\">\n<div class=\"section-content\">\n<div class=\"image\">\n <img decoding=\"async\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/llm-section-2-1.png\" alt=\"History of Finetuning LLM?\" title=\"History of Finetuning LLM?\">\n <\/div>\n<div class=\"item\">\n<h2>History of Finetuning LLM?<\/h2>\n<p>The history of fine-tuning large language models (LLMs) traces back to the evolution of deep learning and natural language processing (NLP). Initially, models like Word2Vec and GloVe laid the groundwork for understanding word embeddings. The introduction of transformer architectures in 2017, particularly with the release of BERT (Bidirectional Encoder Representations from Transformers), marked a significant turning point, allowing for pre-trained models that could be fine-tuned on specific tasks with relatively small datasets. This approach gained traction as researchers recognized the efficiency and effectiveness of leveraging pre-trained models, leading to the development of various LLMs such as GPT-2, GPT-3, and T5. Fine-tuning became a standard practice, enabling these models to adapt to diverse applications, from sentiment analysis to machine translation, while significantly reducing the time and resources needed for training from scratch.<\/p>\n<p>**Brief Answer:** The history of fine-tuning LLMs began with early word embedding techniques, evolving through the introduction of transformer models like BERT in 2017. This allowed for efficient pre-training followed by task-specific fine-tuning, which has since become a standard practice in NLP, facilitating the adaptation of models like GPT-2 and GPT-3 to various applications.\n<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"section-content\">\n<div class=\"item\">\n<h2>Advantages and Disadvantages of Finetuning LLM?<\/h2>\n<p>Fine-tuning large language models (LLMs) offers several advantages and disadvantages. On the positive side, fine-tuning allows for the customization of a pre-trained model to specific tasks or domains, enhancing its performance and relevance in specialized applications. This process can lead to improved accuracy, better understanding of context, and more relevant outputs tailored to user needs. However, there are also notable drawbacks; fine-tuning can be resource-intensive, requiring significant computational power and time, which may not be feasible for all users. Additionally, if not done carefully, fine-tuning can lead to overfitting, where the model becomes too specialized and loses its generalization capabilities. Balancing these factors is crucial for effectively leveraging LLMs in various applications.<\/p>\n<p>**Brief Answer:** Fine-tuning LLMs enhances task-specific performance and relevance but can be resource-intensive and risk overfitting, necessitating careful management.\n<\/p>\n<\/p><\/div>\n<div class=\"image\">\n <img decoding=\"async\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/llm-section-2-2.png\" alt=\"Advantages and Disadvantages of Finetuning LLM?\" title=\"Advantages and Disadvantages of Finetuning LLM?\">\n <\/div>\n<\/p><\/div>\n<div class=\"section-content\">\n<div class=\"image\">\n <img decoding=\"async\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/llm-section-2-3.png\" alt=\"Benefits of Finetuning LLM? \" title=\"Benefits of Finetuning LLM? \">\n <\/div>\n<div class=\"item\">\n<h2>Benefits of Finetuning LLM? <\/h2>\n<p><lu>Fine-tuning large language models (LLMs) offers several significant benefits that enhance their performance and applicability across various tasks. By adjusting a pre-trained model on a specific dataset, fine-tuning allows the model to better understand domain-specific language, nuances, and context, leading to improved accuracy and relevance in its outputs. This process can significantly reduce the amount of data and computational resources needed compared to training a model from scratch. Additionally, fine-tuned LLMs can be tailored to meet the unique requirements of different industries, such as healthcare or finance, enabling more effective communication and decision-making. Overall, fine-tuning enhances the versatility and efficiency of LLMs, making them powerful tools for specialized applications.<\/p>\n<p>**Brief Answer:** Fine-tuning LLMs improves their accuracy and relevance for specific tasks by adapting them to domain-specific language and context, requiring less data and resources than training from scratch, and enhancing their applicability across various industries.<br \/>\n<\/lu><\/p>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"section-content\">\n<div class=\"item\">\n<h2>Challenges of Finetuning LLM?<\/h2>\n<p>Fine-tuning large language models (LLMs) presents several challenges that researchers and practitioners must navigate. One significant challenge is the requirement for substantial computational resources, as LLMs often have billions of parameters that need to be adjusted during the fine-tuning process. This can lead to high costs and longer training times, making it less accessible for smaller organizations or individual developers. Additionally, ensuring that the fine-tuned model generalizes well to new tasks without overfitting on the fine-tuning dataset is crucial; this requires careful selection of training data and hyperparameter tuning. Furthermore, there are concerns about biases present in the pre-trained models, which can be exacerbated during fine-tuning if not properly managed. Finally, the lack of standardized evaluation metrics for specific tasks can complicate the assessment of a model’s performance post-fine-tuning.<\/p>\n<p>**Brief Answer:** Fine-tuning LLMs involves challenges such as high computational costs, risk of overfitting, management of biases, and lack of standardized evaluation metrics, making it a complex process that requires careful consideration and resources.\n<\/p>\n<\/p><\/div>\n<div class=\"image\">\n <img decoding=\"async\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/llm-section-2-4.png\" alt=\"Challenges of Finetuning LLM?\" title=\"Challenges of Finetuning LLM?\">\n <\/div>\n<\/p><\/div>\n<div class=\"section-content\">\n<div class=\"image\">\n <img decoding=\"async\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/llm-section-2-5.png\" alt=\"Find talent or help about Finetuning LLM?\" title=\"Find talent or help about Finetuning LLM?\">\n <\/div>\n<div class=\"item\">\n<h2>Find talent or help about Finetuning LLM?<\/h2>\n<p>Finding talent or assistance for fine-tuning large language models (LLMs) is crucial for organizations looking to leverage these powerful tools effectively. This process involves customizing pre-trained models to better suit specific tasks or domains, which can significantly enhance their performance and relevance. To locate skilled professionals, consider reaching out through platforms like LinkedIn, specialized AI forums, or academic institutions with strong machine learning programs. Additionally, engaging with communities on GitHub or participating in hackathons can help identify individuals with the necessary expertise. Collaborating with consultants or firms specializing in AI can also provide valuable insights and resources for successful fine-tuning.<\/p>\n<p>**Brief Answer:** To find talent for fine-tuning LLMs, explore platforms like LinkedIn, AI forums, and academic institutions, or engage with communities on GitHub. Consulting firms specializing in AI can also offer valuable assistance.\n<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"mpg-section-2\" style=\"background: #e8f1fc;\">\n<div class=\"container\">\n<div class=\"section-content\">\n<div class=\"text\">\n<h2>\n Easiio development service<br \/>\n <\/h2>\n<p>\n Easiio stands at the forefront of technological innovation, offering a comprehensive suite of software development services tailored to meet the demands of today’s digital landscape. Our expertise spans across advanced domains such as Machine Learning, Neural Networks, Blockchain, Cryptocurrency, Large Language Model (LLM) applications, and sophisticated algorithms. By leveraging these cutting-edge technologies, Easiio crafts bespoke solutions that drive business success and efficiency. To explore our offerings or to initiate a service request, we invite you to visit our software development page.\n <\/p>\n<div class=\"button\">\n <button type=\"button\" class=\"contact-btn\"><a class=\"contact\" href=\"\">Contact us<\/a><\/button><br \/>\n <button type=\"button\" class=\"development-service-btn\"><a class=\"development-service\" href=\"https:\/\/www.easiio.com\/development-service\">Easiio development service<\/a><\/button><br \/>\n <button type=\"button\" class=\"meeting-btn\"><a class=\"meeting\" href=\"https:\/\/calendly.com\/jian-lin\/easiio-ai-seo-intro\">Schedule a meeting<\/a><\/button>\n <\/div>\n<\/p><\/div>\n<div class=\"image\">\n <img decoding=\"async\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/crypto-section-3.svg\" alt=\"banner\" title=\"banner\">\n <\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"mpg-section-9\">\n<div class=\"container\">\n<div class=\"title\">\n<h2>FAQ<\/h2>\n<\/p><\/div>\n<ul>\n<div class=\"item\">\n<div class=\"question\">\n What is a Large Language Model (LLM)?<br \/>\n <img decoding=\"async\" class=\"icon-minus\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/open.svg\" alt=\"\">\n <\/div>\n<li class=\"answer\">\n LLMs are machine learning models trained on large text datasets to understand, generate, and predict human language.\n <\/li>\n<\/p><\/div>\n<div class=\"item\">\n<div class=\"question\">\n What are common LLMs?<br \/>\n <img decoding=\"async\" class=\"icon-minus\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/open.svg\" alt=\"\">\n <\/div>\n<li class=\"answer\">\n Examples of LLMs include GPT, BERT, T5, and BLOOM, each with varying architectures and capabilities.\n <\/li>\n<\/p><\/div>\n<div class=\"item\">\n<div class=\"question\">\n How do LLMs work?<br \/>\n <img decoding=\"async\" class=\"icon-minus\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/open.svg\" alt=\"\">\n <\/div>\n<li class=\"answer\">\n LLMs process language data using layers of neural networks to recognize patterns and learn relationships between words.\n <\/li>\n<\/p><\/div>\n<div class=\"item\">\n<div class=\"question\">\n What is the purpose of pretraining in LLMs?<br \/>\n <img decoding=\"async\" class=\"icon-minus\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/open.svg\" alt=\"\">\n <\/div>\n<li class=\"answer\">\n Pretraining teaches an LLM language structure and meaning by exposing it to large datasets before fine-tuning on specific tasks.\n <\/li>\n<\/p><\/div>\n<div class=\"item\">\n<div class=\"question\">\n What is fine-tuning in LLMs?<br \/>\n <img decoding=\"async\" class=\"icon-minus\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/open.svg\" alt=\"\">\n <\/div>\n<li class=\"answer\">\n ine-tuning is a training process that adjusts a pre-trained model for a specific application or dataset.\n <\/li>\n<\/p><\/div>\n<div class=\"item\">\n<div class=\"question\">\n What is the Transformer architecture?<br \/>\n <img decoding=\"async\" class=\"icon-minus\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/open.svg\" alt=\"\">\n <\/div>\n<li class=\"answer\">\n The Transformer architecture is a neural network framework that uses self-attention mechanisms, commonly used in LLMs.\n <\/li>\n<\/p><\/div>\n<div class=\"item\">\n<div class=\"question\">\n How are LLMs used in NLP tasks?<br \/>\n <img decoding=\"async\" class=\"icon-minus\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/open.svg\" alt=\"\">\n <\/div>\n<li class=\"answer\">\n LLMs are applied to tasks like text generation, translation, summarization, and sentiment analysis in natural language processing.\n <\/li>\n<\/p><\/div>\n<div class=\"item\">\n<div class=\"question\">\n What is prompt engineering in LLMs?<br \/>\n <img decoding=\"async\" class=\"icon-minus\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/open.svg\" alt=\"\">\n <\/div>\n<li class=\"answer\">\n Prompt engineering involves crafting input queries to guide an LLM to produce desired outputs.\n <\/li>\n<\/p><\/div>\n<div class=\"item\">\n<div class=\"question\">\n What is tokenization in LLMs?<br \/>\n <img decoding=\"async\" class=\"icon-minus\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/open.svg\" alt=\"\">\n <\/div>\n<li class=\"answer\">\n Tokenization is the process of breaking down text into tokens (e.g., words or characters) that the model can process.\n <\/li>\n<\/p><\/div>\n<div class=\"item\">\n<div class=\"question\">\n What are the limitations of LLMs?<br \/>\n <img decoding=\"async\" class=\"icon-minus\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/open.svg\" alt=\"\">\n <\/div>\n<li class=\"answer\">\n Limitations include susceptibility to generating incorrect information, biases from training data, and large computational demands.\n <\/li>\n<\/p><\/div>\n<div class=\"item\">\n<div class=\"question\">\n How do LLMs understand context?<br \/>\n <img decoding=\"async\" class=\"icon-minus\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/open.svg\" alt=\"\">\n <\/div>\n<li class=\"answer\">\n LLMs maintain context by processing entire sentences or paragraphs, understanding relationships between words through self-attention.\n <\/li>\n<\/p><\/div>\n<div class=\"item\">\n<div class=\"question\">\n What are some ethical considerations with LLMs?<br \/>\n <img decoding=\"async\" class=\"icon-minus\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/open.svg\" alt=\"\">\n <\/div>\n<li class=\"answer\">\n Ethical concerns include biases in generated content, privacy of training data, and potential misuse in generating harmful content.\n <\/li>\n<\/p><\/div>\n<div class=\"item\">\n<div class=\"question\">\n How are LLMs evaluated?<br \/>\n <img decoding=\"async\" class=\"icon-minus\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/open.svg\" alt=\"\">\n <\/div>\n<li class=\"answer\">\n LLMs are often evaluated on tasks like language understanding, fluency, coherence, and accuracy using benchmarks and metrics.\n <\/li>\n<\/p><\/div>\n<div class=\"item\">\n<div class=\"question\">\n What is zero-shot learning in LLMs?<br \/>\n <img decoding=\"async\" class=\"icon-minus\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/open.svg\" alt=\"\">\n <\/div>\n<li class=\"answer\">\n Zero-shot learning allows LLMs to perform tasks without direct training by understanding context and adapting based on prior learning.\n <\/li>\n<\/p><\/div>\n<div class=\"item\">\n<div class=\"question\">\n How can LLMs be deployed?<br \/>\n <img decoding=\"async\" class=\"icon-minus\" src=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/open.svg\" alt=\"\">\n <\/div>\n<li class=\"answer\">\n LLMs can be deployed via APIs, on dedicated servers, or integrated into applications for tasks like chatbots and content generation.\n <\/li>\n<\/p><\/div>\n<\/ul><\/div>\n<\/p><\/div>\n<p> <?php get_footer('contact');?>\n<\/div>\n<p><?php get_footer('easiio');?><\/p>\n<p><?php wp_footer(); ?><\/p>\n<p><script src=\"https:\/\/www.easiio.com\/wp-content\/themes\/easiio\/assets\/js\/mpg-index.js\"><\/script><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Finetuning LLM LLM: Unleashing the Power of Large Language Models History of Finetuning LLM? The history of fine-tuning large language models (LLMs) traces back to the evolution of deep learning and natural language processing (NLP). Initially, models like Word2Vec and GloVe laid the groundwork for understanding word embeddings. The introduction of transformer architectures in 2017, […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"generate-page\/llm-template\/finetuning-llm.php","meta":{"footnotes":""},"class_list":["post-9079","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Finetuning LLM - easiio<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.easiio.com\/finetuning-llm\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Finetuning LLM - easiio\" \/>\n<meta property=\"og:description\" content=\"Finetuning LLM LLM: Unleashing the Power of Large Language Models History of Finetuning LLM? 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The introduction of transformer architectures in 2017, […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.easiio.com\/finetuning-llm\/\" \/>\n<meta property=\"og:site_name\" content=\"easiio\" \/>\n<meta property=\"og:image\" content=\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/llm-section-2-1.png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.easiio.com\/finetuning-llm\/\",\"url\":\"https:\/\/www.easiio.com\/finetuning-llm\/\",\"name\":\"Finetuning LLM - easiio\",\"isPartOf\":{\"@id\":\"https:\/\/www.easiio.com\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.easiio.com\/finetuning-llm\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.easiio.com\/finetuning-llm\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/llm-section-2-1.png\",\"datePublished\":\"2025-02-20T08:29:45+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/www.easiio.com\/finetuning-llm\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.easiio.com\/finetuning-llm\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.easiio.com\/finetuning-llm\/#primaryimage\",\"url\":\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/llm-section-2-1.png\",\"contentUrl\":\"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/llm-section-2-1.png\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.easiio.com\/finetuning-llm\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.easiio.com\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Finetuning LLM\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.easiio.com\/#website\",\"url\":\"https:\/\/www.easiio.com\/\",\"name\":\"easiio\",\"description\":\"\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.easiio.com\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Finetuning LLM - easiio","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.easiio.com\/finetuning-llm\/","og_locale":"en_US","og_type":"article","og_title":"Finetuning LLM - easiio","og_description":"Finetuning LLM LLM: Unleashing the Power of Large Language Models History of Finetuning LLM? The history of fine-tuning large language models (LLMs) traces back to the evolution of deep learning and natural language processing (NLP). Initially, models like Word2Vec and GloVe laid the groundwork for understanding word embeddings. The introduction of transformer architectures in 2017, […]","og_url":"https:\/\/www.easiio.com\/finetuning-llm\/","og_site_name":"easiio","og_image":[{"url":"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/llm-section-2-1.png","type":"","width":"","height":""}],"twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"6 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.easiio.com\/finetuning-llm\/","url":"https:\/\/www.easiio.com\/finetuning-llm\/","name":"Finetuning LLM - easiio","isPartOf":{"@id":"https:\/\/www.easiio.com\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.easiio.com\/finetuning-llm\/#primaryimage"},"image":{"@id":"https:\/\/www.easiio.com\/finetuning-llm\/#primaryimage"},"thumbnailUrl":"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/llm-section-2-1.png","datePublished":"2025-02-20T08:29:45+00:00","breadcrumb":{"@id":"https:\/\/www.easiio.com\/finetuning-llm\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.easiio.com\/finetuning-llm\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.easiio.com\/finetuning-llm\/#primaryimage","url":"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/llm-section-2-1.png","contentUrl":"https:\/\/cdn.easiio.cn\/assets\/images\/easiio_page\/llm-section-2-1.png"},{"@type":"BreadcrumbList","@id":"https:\/\/www.easiio.com\/finetuning-llm\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.easiio.com\/"},{"@type":"ListItem","position":2,"name":"Finetuning LLM"}]},{"@type":"WebSite","@id":"https:\/\/www.easiio.com\/#website","url":"https:\/\/www.easiio.com\/","name":"easiio","description":"","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.easiio.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"_links":{"self":[{"href":"https:\/\/www.easiio.com\/wp-json\/wp\/v2\/pages\/9079","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.easiio.com\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.easiio.com\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.easiio.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.easiio.com\/wp-json\/wp\/v2\/comments?post=9079"}],"version-history":[{"count":0,"href":"https:\/\/www.easiio.com\/wp-json\/wp\/v2\/pages\/9079\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.easiio.com\/wp-json\/wp\/v2\/media?parent=9079"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}