Large language models (LLMs) have enabled AI tools that help you write more code faster, but as we ask these tools to take on more and more complex tasks, there are limitations that become apparent. Challenges such as understanding the nuances of programming languages, complex dependencies, and adapting to codebase-specific context can lead to lower-quality code and cause bottlenecks down the line.
]]>Generative AI, especially with breakthroughs like AlphaFold and RosettaFold, is transforming drug discovery and how biotech companies and research laboratories study protein structures, unlocking groundbreaking insights into protein interactions. Proteins are dynamic entities. It has been postulated that a protein’s native state is known by its sequence of amino acids alone…
]]>Translation plays an essential role in enabling companies to expand across borders, with requirements varying significantly in terms of tone, accuracy, and technical terminology handling. The emergence of sovereign AI has highlighted critical challenges in large language models (LLMs), particularly their struggle to capture nuanced cultural and linguistic contexts beyond English-dominant…
]]>Evaluating large language models (LLMs) and retrieval-augmented generation (RAG) systems is a complex and nuanced process, reflecting the sophisticated and multifaceted nature of these systems. Unlike traditional machine learning (ML) models, LLMs generate a wide range of diverse and often unpredictable outputs, making standard evaluation metrics insufficient. Key challenges include the…
]]>Training and customizing LLMs for high accuracy is fraught with challenges, primarily due to their dependency on high-quality data. Poor data quality and inadequate volume can significantly reduce model accuracy, making dataset preparation a critical task for AI developers. Datasets frequently contain duplicate documents, personally identifiable information (PII), and formatting issues.
]]>In the rapidly evolving landscape of AI, the preparation of high-quality datasets for large language models (LLMs) has become a critical challenge. It directly affects a model’s accuracy, performance, and ability to generate reliable and unbiased outputs across diverse tasks and domains. Thanks to the partnership between NVIDIA and Dataloop, we are addressing this obstacle head-on…
]]>In the rapidly evolving field of medicine, the integration of cutting-edge technologies is crucial for enhancing patient care and advancing research. One such innovation is retrieval-augmented generation (RAG), which is transforming how medical information is processed and used. RAG combines the capabilities of large language models (LLMs) with external knowledge retrieval…
]]>Equipping agentic AI applications with tools will usher in the next phase of AI. By enabling autonomous agents and other AI applications to fetch real-time data, perform actions, and interact with external systems, developers can bridge the gap to new, real-world use cases that significantly enhance productivity and the user experience. xpander AI, a member of the NVIDIA Inception program for…
]]>Developing a high-performing Hebrew large language model (LLM) presents distinct challenges stemming from the rich and complex nature of the Hebrew language itself. The intricate structure of Hebrew, with words formed through root and pattern combinations, demands sophisticated modeling approaches. Moreover, the lack of capitalization and the frequent absence of punctuation like periods and commas…
]]>In the rapidly evolving landscape of AI-driven applications, re-ranking has emerged as a pivotal technique to enhance the precision and relevance of enterprise search results. By using advanced machine learning algorithms, re-ranking refines initial search outputs to better align with user intent and context, thereby significantly improving the effectiveness of semantic search.
]]>Multilingual large language models (LLMs) are increasingly important for enterprises operating in today’s globalized business landscape. As businesses expand their reach across borders and cultures, the ability to communicate effectively in multiple languages is crucial for success. By supporting and investing in multilingual LLMs, enterprises can break down language barriers, foster inclusivity…
]]>In Part 1, we discussed how to train a monolingual tokenizer and merge it with a pretrained LLM’s tokenizer to form a multilingual tokenizer. In this post, we show you how to integrate the customized tokenizer into the pretrained LLM as well as how to start a continual pretraining task in NVIDIA NeMo. Please import the following libraries before starting: After…
]]>In today’s globalized world, the ability of AI systems to understand and communicate in diverse languages is increasingly crucial. Large language models (LLMs) have revolutionized the field of natural language processing, enabling AI to generate human-like text, answer questions, and perform various language tasks. However, most mainstream LLMs are trained on data corpora that primarily consist of…
]]>In the first post, we walked through the prerequisites for a neural machine translation example from English to Chinese, running the pretrained model with NeMo, and evaluating its performance. In this post, we walk you through curating a custom dataset and fine-tuning the model on that dataset. Custom data collection is crucial in model fine-tuning because it enables a model to adapt to…
]]>Neural machine translation (NMT) is an automatic task of translating a sequence of words from one language to another. In recent years, the development of attention-based transformer models has had a profound impact on complicated language modeling tasks, which predict the next upcoming token in the sentence. NMT is one of the typical instances. There are plenty of open-source NMT models…
]]>Retrieval-augmented generation (RAG) is a technique that combines information retrieval with a set of carefully designed system prompts to provide more accurate, up-to-date, and contextually relevant responses from large language models (LLMs). By incorporating data from various sources such as relational databases, unstructured document repositories, internet data streams, and media news feeds…
]]>Large language models (LLMs) have revolutionized natural language processing (NLP) with their ability to learn from massive amounts of text and generate fluent and coherent texts for various tasks and domains. However, customizing LLMs is a challenging task, often requiring a full training process that is time-consuming and computationally expensive. Moreover, training LLMs requires a diverse and…
]]>Large language models (LLMs) have revolutionized the field of AI, creating entirely new ways of interacting with the digital world. While they provide a good generalized solution, they often must be tuned to support specific domains and tasks. AI coding assistants, or code LLMs, have emerged as one domain to help accomplish this. By 2025, 80% of the product development lifecycle will make…
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