ArunKumar R’s Post

🔒Unlock the power hidden in the diversity of Large Language Models (LLMs). Understand why the world of GenAI needs multiple LLM families 👇 In the ever-evolving landscape of Generative AI, understanding the diversity of Large Language Models (LLMs) is key. Models like GPT, Llama, and Mistral each bring unique strengths to various applications, from coding to natural language processing. Each LLM family is built with specific goals, whether it's optimizing for performance, size, or safety. For instance, GPT models are known for their generative capabilities, while Llama models focus on safety with lower violation rates. The diversity in architecture, parameter size, and training datasets means no single model excels at all tasks. Choosing the right LLM is critical. Just as in machine learning where different models excel with different data types, LLMs are designed to address specific needs. Whether it's a small-scale task that a lighter model can handle or a complex problem requiring a robust model, understanding these differences allows for better, more efficient use of resources. This multiplicity not only drives innovation but ensures that as our needs evolve, so too do our tools. You can read more about this on Mehul Gupta article: https://lnkd.in/ec42B83E #AI #MachineLearning #LLMs #GenAI #TechInnovation

Hugging Face leads the way in the open-source AI movement! Let's learn more about Hugging Face: "GitHub of the ML World" They refer to themselves as "The AI community for building the future" FYI, Hugging Face is a collaborative platform with tools that help anyone to build, train, and use NLP and ML models using open-source code. It helps companies release AI models, datasets, and tools. Take a look at these helpful features and tools offered by Hugging Face: 1. Model Hub: Tools that allowed developers to freely download, fine-tune, and even merge different models freely and efficiently. 2. Transformers Library: State-of-the-art machine learning models made for NLP tasks such as text classification, language creation, translation, summarization, and more. 3. Datasets and tokenizers: Offers a huge collection of datasets and easy-to-use tokenizers designed for the Transformers library. Want to know more, visit: https://huggingface.co/ #opensource #artificialintelligence #deeplearning #machinelearning #opensourcerevolution #huggingface #llms #largelanguagemodels #largemodels #datasets #aimodels #ai #tokenizers

Hey everyone! It's been a while since I dived into the latest in generative AI and NLP, and boy, has it been an exciting journey catching up! As I refresh my knowledge and pick up some new concepts, I decided to share a few snippets here with you all: So, in the world of Large Language Models (LLMs), there are three main types of model architectures: - Encoder-only Models: These are all about grasping the context of language. Think of them as the part of the Transformer architecture that really 'understands' what the input text is all about. These include models like BERT and RoBERTa that are great at tasks like text classification and named-entity recognition, thanks to their knack for understanding the nitty-gritty of text. - Decoder-only Models: Now, these are the ones that focus on generating text. Models like BLOOM, LLama, and the fan-favourite GPT family, are perfect for generalized tasks like summarization and Q&A. There is a common misconception that GPT like traditional transformers has an encoder, which is understandable because the T stands for transformer in GPT. However, unlike the transformer introduced in "Attention Is All You Need", the GPT family uses a decoder-only architecture. Another subtle, but important deviation is that decoder-only models use masked self-attention instead of the traditional self-attention. More on this later. - Encoder-Decoder Models: And then we have the combination, like BART, T5, and the recent Google Gemini. These models combine both encoders and decoders, giving them serious flexibility across different tasks. But complexity comes with a cost. Having both an encoder and decoder means longer training times and higher computational costs compared to decoder-only setups. Hope you find this breakdown helpful! Can't wait to share more soon! #AI #NLP #MachineLearning #ArtificialIntelligence #LanguageModels #Transformer #GenerativeAI #NaturalLanguageProcessing

📚 Text summarization has taken a significant leap forward with the innovative "Chain of Density" (CoD) technique, developed by researchers from Columbia University, MIT, and Salesforce AI. This approach uses generative AI to create summaries that are both concise and rich in detail. 🔍 What is Chain of Density? The CoD method generates increasingly dense summaries by iteratively incorporating more informative entities without increasing the summary's length. This process enhances the summary’s informativeness while maintaining readability. 📊 Key Findings: Entity Density: Starting with a sparse summary, CoD increases entity density step-by-step, eventually surpassing human-written summaries. Human Preferences: Studies show that people prefer denser summaries generated by CoD, finding them almost as detailed and informative as those written by humans. 🔬 Why It Matters: In applications where real-time, accurate, and detailed information is critical, CoD offers a robust solution. Whether in news, research, or business, this method can transform how we consume and utilize large volumes of information. 🔗 For further details, see the full technical report available on arXiv: https://lnkd.in/gdJPPwSw P.s. Thank you Dr. Héctor Allende-Cid for telling me about CoD. #AI #MachineLearning #NLP #GPT4 #TextSummarization #Innovation #Research #DataScience #generativeai

You can’t train the AI, accept it! I often hear people talk about “training” models, even tech professionals, but most of them are surprised when they realize they can’t actually do it themselves. Here are some examples: For image-based models: • Simple classification (which is outdated): 5,000 to 50,000 images. • Complex tasks (e.g., object detection): 100,000–10,000,000+ images. For NLP models: • Simple tasks (e.g., sentiment analysis): 10,000–100,000 text samples. • Complex tasks (e.g., language modeling): 10 million–10+ billions text samples. As you can see, the data requirements are massive. Few companies have access to such large datasets, and even fewer have the domain-specific data needed. So, how do you adapt a model to your company’s needs if you can’t train it from scratch? Fine-tuning is your solution. You can adjust pre-trained models for specific tasks without starting from zero. It’s still challenging but far more achievable. For image-based models: • Simple classification: 500–5,000 images. • Complex tasks: 10,000–50,000 images. For NLP: • Simple tasks: 500–10,000 text samples. • Complex tasks: 10,000–50,000 text samples. The key takeaway: Find a pre-trained model close to your domain, then fine-tune it if necessary. It’s ambitious but realistic for most organizations. #AI #LLM #ML #PreTrainedModel

As transformer architectures have increasingly dominated the machine learning landscape in recent years, they’ve also revived an old but important debate regarding interpretability and transparency in #AI. BertViz is an explainability tool in a field (#NLP) that is otherwise notoriously opaque. And, despite its name, BertViz doesn’t only work on BERT. Learn more, including how to log BertViz using Comet's custom panels: #Transformers #LLMs #LLMOps Comet

Generative AI has taken a boom in recent times. With the advent of Foundation models such as Large Language models (LLMs), generative AI has shown beyond creative outcomes. It's amazing to see how far AI and machine learning have gone. There are some great use case where we are seeing Foundation Models being leveraged. #GenAI #ai #cioinsights https://lnkd.in/d-rbAgwT

Llama-3.1-Storm-8B: A Groundbreaking AI Model that Outperforms Meta AI’s Llama-3.1-8B-Instruct and Hermes-3-Llama-3.1-8B Models on Diverse Benchmarks Artificial intelligence (AI) has witnessed rapid advancements over the past decade, with significant strides in NLP, machine learning, and deep learning. Among the latest and most notable developments is the release of Llama-3.1-Storm-8B by Ashvini Kumar Jindal and team. This new AI model represents a considerable leap forward in language model capabilities, setting new benchmarks in performance, efficiency, and applicability across various industries. One of the standout features of Llama-3.1-Storm-8B is its scale. With 8 billion parameters, the model is significantly more powerful than many competitors. This massive scale allows the model to capture subtle nuances in language, making it capable of generating text that is not only contextually relevant but also grammatically coherent and stylistically appropriate. The model’s architecture is based on a transformer design, which has become the standard in modern NLP due to its ability to handle long-range dependencies in text data. Llama-3.1-Storm-8B has been optimized for performance, balancing the trade-off between computational efficiency and output quality. This optimization is particularly important in scenarios requiring real-time processing, such as live chatbots or automated transcription services. The model’s ability to generate high-quality text in real-time without significant latency makes it an ideal choice for businesses looking to implement AI-driven solutions that require quick and accurate responses.... Read our full take on this: https://lnkd.in/gZB9UdtY Model: https://lnkd.in/gYd5s6AS Ashvini Jindal Ankur Parikh Pawan Rajpoot

Pipeline Blueprint - RAG Flow ☑ The diagram is a workflow for a Retrieval-Augmented Generation (RAG) system for AI applications. ☑ Enterprise data is collected from various sources, including documents, emails, chats, and business databases. ☑ Data is processed and indexed for efficient retrieval. ☑ User queries are turned into vector representations using an embedding model. ☑ The system retrieves and ranks information relevant to the query from the index. ☑ Large language or multimodal models (LLM/LMM) generate responses using the retrieved data. ☑ A multi-step generation process allows for iterative refinement of responses. ☑ Guardrails are in place to ensure content quality and appropriateness. ☑ Responses may be post-processed for further refinement. ☑ User feedback is collected to improve the system. ☑ Feedback leads to tagged data, which is used for model fine-tuning. ☑ The fine-tuned models are stored in a model repository for future use. ☑ The system uses a feedback loop to continually improve through user interaction. Overall, this pipeline represents a sophisticated AI system that learns from interactions to improve over time, using a combination of retrieval from a large corpus of data and generative AI to provide useful responses to user queries. Image Credit : https://opea.dev/ #Genai #LLM #RAG #NLP #machinelearning #datascience #ai #deeplearning

🚀 Understanding Transformers in AI: A Simple Breakdown 🤖 Transformers are revolutionizing the world of NLP and AI. Here's a quick guide to understand how this architecture works! 🌟 🔍 Encoder: The encoder processes the input data and generates meaningful representations for further use. Input Embedding: Converts input tokens (words, phrases) into vector representations 🔡➡️📊. Positional Encoding: Adds information about the sequence of words 🔢. Multi-Head Attention: Allows tokens to focus on other relevant tokens in the sequence 🧠. Add & Norm: Stabilizes learning using residual connections and layer normalization ⚖️. Feed Forward Network: A simple fully connected network to process the data 🔄. N Layers: The encoder repeats this process N times for richer representation 🔁. 🔍 Decoder: The decoder generates the final output by understanding and focusing on the input provided by the encoder. Output Embedding: Converts the generated tokens into vectors for decoding 📝➡️📊. Masked Multi-Head Attention: Ensures the model can’t "peek" at future tokens 🔒. Add & Norm: Normalizes outputs to ensure smooth training 💡. Multi-Head Attention: Focuses on important parts of the encoded input sequence 🎯. N Layers: Like the encoder, the decoder repeats N times 🌀. 🔑 Final Output: Linear Layer: Converts the output to the right size 📏. Softmax: Generates probabilities for the next word/token prediction 🧮. Transformers power popular models like BERT, GPT, M2M, Llama3, Mistrial, Gemini, and T5, driving incredible advances in AI. 💡💥 GitLink:[https://lnkd.in/gSpwqx57] Follow☝️☝️☝️ #AI #NLP #Transformers #MachineLearning #DeepLearning #DataScience #Innovation #Tech #OpenAI #LargeLanaguageModel #FineTuning #PEFT Data Science AI Learner Community Data Science AI Learner Community