Karna AI Automation’s Post

In the present day, differentiating between Large Language Models (LLMs) and Large Multimodal Models (LMMs) is a prerequisite for understanding their distinct roles in AI. Large Language Models (LLMs) specialize in processing and generating human language, excelling in tasks like text generation, translation, and sentiment analysis. Their core competence lies in linguistic capabilities, making them pivotal for natural language processing. Conversely, the Large Multimodal Models (LMMs) integrate and interpret information across various modalities—such as text, images, and audio—offering a comprehensive understanding of complex inputs. They are adept at tasks requiring cross-modal reasoning, including image captioning and visual question answering. The distinction shows the progression in AI research: LLMs advance language-based tasks, while LMMs expand AI's capacity for multifaceted data interactions. What are your thoughts on the future impact of LMMs in AI? #multimodalai #futureofai #artificialintelligence #aimodels #letsconnect #deeplearning #machinelearning #largelanguagemodels

Prompt engineering Day 2: What is LLM or Large Language Model? A Large Language Model (LLM) is a type of artificial intelligence model designed to understand and generate human-like text. These models are trained on vast amounts of text data, learning the patterns and structures of language in order to generate coherent and contextually relevant responses. Large Language Models, like OpenAI's GPT series (Generative Pre-trained Transformer), are capable of understanding and generating text in various languages and across a wide range of topics. LLMs are characterized by their size, as they typically consist of millions or even billions of parameters, which are the trainable elements that allow the model to understand and generate text. The large size of these models enables them to capture intricate nuances of language and produce high-quality responses that closely resemble human-written text. Applications of Large Language Models include text generation, translation, summarization, question answering, and more. They are used in various fields such as natural language processing, conversational AI, content generation, and even creative writing assistance. #PromptEngineering #EngineeringCourse #STEMeducation #LearnEngineering #ProblemSolvingSkills #CreativeThinking #InnovationInEngineering #EngineeringSkills #ProfessionalDevelopment #EngineeringStudents #STEMlearning #EngineeringCommunity #TechEducation #FutureEngineers #EngineeringChallenge #LLM #largelanguagemodel

I'm thrilled to share an innovative tool that's transforming the world of language translation: DeepL. Leveraging advanced neural network technology, DeepL offers precise and natural translations, making it an indispensable asset for professionals, students, and anyone in need of efficient language solutions. Here are the standout features of DeepL: 1. Advanced Neural Network: Provides highly accurate and natural translations. 2. Extensive Language Support: Compatible with over 130 languages, catering to diverse global needs. 3. Professional and Personal Use: Ideal for a range of users, from business professionals to students. 4. DeepL Write: An additional feature that offers text corrections and suggestions to enhance your writing. Embrace the power of technology with DeepL and elevate your communication across languages! #TranslationTech #AI #DeepL #LanguageSolutions #Innovation

Large Language Models (LLMs) have revolutionized the field of natural language processing, enabling users to interact with #AI systems through direct prompting. However, the majority of #LLMs are trained on English-centric datasets, leading to disparities in performance when processing non-English prompts. To realize our vision of democratizing artificial intelligence, LLM applications must reach the last mile user, especially outside the anglosphere. In this blog, explore the challenges non-English users face and presents a prompt engineering solution in the form of Translation Augmented Generation. Read more ➡ https://lnkd.in/eKefEXWU Authors: Raghavan Muthuregunathan, Jigisha Mavani #opensource #lfaidata #linuxfoundation

List of different Hugging Face models along with their applications: BERT (Bidirectional Encoder Representations from Transformers): Natural Language Understanding (NLU) Sentiment Analysis Text Classification GPT (Generative Pre-trained Transformer): Text Generation Chatbots Language Translation RoBERTa (Robustly optimized BERT approach): Text Classification Named Entity Recognition (NER) Question Answering DistilBERT: Lightweight version of BERT for efficient deployment Text Classification Text Similarity T5 (Text-to-Text Transfer Transformer): Multitask Learning Language Translation Summarization Electra: Text Classification Named Entity Recognition (NER) Text Generation BART (Bidirectional and Auto-Regressive Transformers): Text Summarization Text Generation Language Translation BlenderBot: Conversational AI Chatbots Dialogue Systems These models are widely used in various natural language processing tasks across industries and research domains. #llms #huggingface

🌟 Exploring the Power of Large Language Models (LLMs)! 🌟 I'm thrilled to share some insights about Large Language Models (LLMs), which are revolutionizing the field of artificial intelligence and natural language processing. These models, like OpenAI's GPT-4, are designed to understand, generate, and translate human language in a way that feels incredibly natural and human-like. 🔹 Key Features of LLMs: Comprehension: Understands complex queries and provides detailed responses. Generation: Creates coherent and contextually relevant text. Translation: Translates languages with high accuracy. Summarization: Condenses long documents into concise summaries. 🔹 Applications of LLMs: Customer support automation Content creation and curation Language translation services Research and data analysis Educational tools and tutoring 🔹 Future of LLMs: The potential of LLMs is immense, with continuous advancements opening new horizons in AI. Their ability to learn and adapt from vast datasets is paving the way for more sophisticated and personalized AI-driven solutions. Let's embrace this exciting journey into the future of AI with LLMs at the forefront! 🚀 #AI #MachineLearning #NaturalLanguageProcessing #Innovation #TechTrends #FutureOfWork

𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 https://zurl.co/uuTR Dive into the world of Natural Language Processing with top courses designed for every skill level! Whether you're a beginner or an advanced learner, these programs offer hands-on projects and real-world applications. #NaturalLanguageProcessingCourses #CoursesforNaturalLanguageProcessing #CourserasNaturalLanguageProcessingSpecialization #UdacitysMasterNaturalLanguageProcessing #SpaCysAdvancedNLP #AI #AINews #AnalyticsInsight #AnalyticsInsightMagazine

We're thrilled to introduce our novel approach, OLoRA: Orthonormal Low-Rank Adaptation of Large Language Models. What is OLoRA? OLoRA stands for Orthonormal Low Rank Adaptation, an approach that converges significantly faster and ultimately achieves superior performance compared to the state-of-the-art LoRA method. The Problem with LoRA While LoRA has been a popular choice for adapting large language models, it has its limitations. It can be computationally expensive, slow to converge, and may not always achieve optimal results. The OLoRA Advantage Our OLoRA method addresses these limitations by introducing orthonormal constraints, which enable faster convergence and improved performance. This means that OLoRA can adapt large language models more efficiently and effectively, unlocking new possibilities for natural language processing and AI applications. Paper: https://lnkd.in/d3yNF5pT #OLoRA #LargeLanguageModels #AIResearch #Innovation #NaturalLanguageProcessing #ConversationalAI

Anna Gutowska: "An artificial intelligence (AI) agent refers to a system or program that is capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and utilizing available tools. AI agents can encompass a wide range of functionalities beyond natural language processing including decision-making, problem-solving, interacting with external environments and executing actions. These agents can be deployed in various applications to solve complex tasks in various enterprise contexts from software design and IT automation to code-generation tools and conversational assistants. They use the advanced natural language processing techniques of large language models (LLMs) to comprehend and respond to user inputs step-by-step and determine when to call on external tools."