Generative AI

🚀 Introducing Multimodal Llama 3.2 by AI at Meta & DeepLearning.AI ! 🌍 Meta and DeepLearning.AI have just released a course on Llama 3.2, a cutting-edge model built to handle text and image inputs with remarkable efficiency and versatility. From analyzing images to calling external tools, Llama 3.2 takes multimodal AI to the next level. Key features: 🧠 Pretrained Models: Up to 90B parameters, supporting both text-only and text+image tasks. 🎓 Instruction-tuned Models: Follows user commands with increased accuracy. 🌐 Multilingual Capabilities: Supporting English, French, Hindi, Spanish, and more. 🔄 128K-Token Vocabulary: Improving efficiency for larger prompts and boosting performance. Some exciting use cases: Image-to-code conversion Grading math homework from photos Nutrition facts analysis from images and more! With advanced tool calling, built-in memory orchestration, and strong multimodal safety features, Llama 3.2 is designed to handle real-world tasks with precision. 🔧💡 #llm #genai #masteringllm #llama2 #meta

Get {50%} 𝗢𝗙𝗙 (𝗖𝗼𝗱𝗲 - LLM50) on our 𝗟𝗟𝗠 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽 𝗖𝗼𝘂𝗿𝘀𝗲 - https://lnkd.in/dPJTm5bR =============================  How to Build a 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) 𝗦𝘆𝘀𝘁𝗲𝗺 𝗘𝗻𝘁𝗶𝗿𝗲𝗹𝘆 on Snowflake Ever wondered how to implement a RAG system within a single platform? Snowflake has made it easier than ever by bringing together all the key components needed to build a continuously updating RAG system. Their standout feature? Snowflake Cortex, which seamlessly integrates LLM capabilities into your workflow. Let’s break it down step-by-step: Here's what the RAG pipeline looks like on Snowflake, including how to query your internal knowledge base with a chatbot UI: 1. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗮𝗻𝗱 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴: First, you need to convert internal documents into a format that’s optimized for querying. This is done using embeddings, and Snowflake makes it simple: • Load your internal data into Snowflake’s Stage. • Chunk the data using Snowpark. • Leverage Snowflake Cortex’s LLM embedding models to transform those chunks into vector embeddings. • Store the embeddings in Snowflake’s Vector Storage. 2. 𝗔𝗻𝘀𝘄𝗲𝗿𝗶𝗻𝗴 𝗤𝘂𝗲𝗿𝗶𝗲𝘀: Now you can start building responses to questions based on the stored data: • Embed the question/query using the same embedding model from Snowflake Cortex that was used on your knowledge base. • Run a query using the resulting vector embedding against the stored data (step 4). Snowflake Cortex handles this seamlessly. • Once you retrieve the most relevant context, pass the original question along with the retrieved data to the LLM, also powered by Snowflake Cortex, to generate a complete answer. 3. 𝗕𝗼𝗻𝘂𝘀 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • Host the chatbot UI using Streamlit directly in Snowflake for an integrated user experience. • Real-time data updates: Snowflake Cortex, via Snowpark, can continuously stream new internal data into the knowledge base, keeping your RAG system always up-to-date. • With everything available on Snowflake—data storage, vector embeddings, LLMs, and even a web UI—you have a one-stop platform for building a highly effective RAG system. Have you tried using Snowflake for RAG? Share your experiences or questions in the comments! Follow Aurimas Griciūnas for such amazing content. #RAG #AI #Snowflake #LLM #MachineLearning #DataScience #NLP #GenerativeAI

Get {50%} 𝗢𝗙𝗙 (𝗖𝗼𝗱𝗲 - LLM50) on our 𝗟𝗟𝗠 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽 𝗖𝗼𝘂𝗿𝘀𝗲 - https://lnkd.in/dPJTm5bR ================================================= 🚀 Exploring 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) Techniques 🔍 Just stumbled upon an incredibly informative repository called "𝗥𝗔𝗚_𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀" by Nir Diamant—a fantastic resource for anyone looking to dive deeper into Retrieval-Augmented Generation (RAG)! 📚 This repo is packed with valuable insights and practical implementations, covering a range of important RAG topics, including: 🔸 Foundational RAG techniques 🔸 Query enhancement strategies 🔸 Context and content enrichment for better responses 🔸 Advanced retrieval methods to improve accuracy 🔸 Iterative and adaptive techniques for dynamic use cases 🔸 Evaluation and explainability for better model understanding 🔸 Advanced RAG architectures to push the limits of what's possible Definitely bookmarking this one as a go-to reference for enhancing RAG workflows! 🧠 Whether you're a beginner or seasoned pro, this is a must-see for expanding your RAG toolkit. Check it out and level up your RAG knowledge! 🚀 #MachineLearning #RAG #RetrievalAugmentedGeneration #NLP #AI #KnowledgeRetrieval

Get {50%} 𝗢𝗙𝗙 (𝗖𝗼𝗱𝗲 - LLM50) on our 𝗟𝗟𝗠 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽 𝗖𝗼𝘂𝗿𝘀𝗲 - https://lnkd.in/dPJTm5bR ================================================== 🚀 Exploring 𝗠𝗶𝘅𝘁𝘂𝗿𝗲 𝗼𝗳 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 (𝗠𝗼𝗘) 𝗶𝗻 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 🔍 Just came across an incredible visual guide to understanding Mixture of Experts (MoE) by Maarten Grootendorst, and it’s a must-read for anyone diving into advanced model architectures! 🎨 Maarten's 𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻 𝗼𝗳 𝗵𝗼𝘄 𝗠𝗼𝗘𝘀 𝘄𝗼𝗿𝗸 makes it super digestible—even for those of us who may not have fully explored MoEs yet. His visualizations are top-notch and simplify this complex topic, covering: 💡 𝗞𝗲𝘆 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀: How MoE selectively activates only parts of the model for each task. ⏳ 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗴𝗮𝗶𝗻𝘀: MoEs allow models to scale efficiently, reducing computational costs. ⚡ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁𝘀: By activating only a subset of “experts,” MoEs drastically improve speed and performance. 💻 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: Why MoEs are used in large-scale models like GPT-3 and Switch Transformers to achieve both scalability and precision. #llm #genai #MoE

Get {50%} 𝗢𝗙𝗙 (𝗖𝗼𝗱𝗲 - LLM50) on our 𝗟𝗟𝗠 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽 𝗖𝗼𝘂𝗿𝘀𝗲 - https://lnkd.in/dPJTm5bR ================================================= When beginning your journey in MLOps, one of the key initial steps is to establish Continuous Integration (CI). Setting this up can be straightforward, as shown in the example below. 𝗧𝗵𝗶𝘀 𝗖𝗜 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗶𝗻𝗰𝗹𝘂𝗱𝗲𝘀: • Pre-commit hooks • Unit tests • Python package build • Docker image build • Docker image push GitLab makes this process seamless, offering a built-in free container registry. Plus, if you're looking to scale later, GitLab also integrates MLflow for more comprehensive workflows. It's important to remember that in the ML lifecycle, CI doesn’t always go hand-in-hand with Continuous Deployment (CD), unlike in traditional software engineering. We don’t always deploy every new model. However, continuously integrating and testing your code remains crucial to ensure smooth progress! Read the entire blog here - https://lnkd.in/dRzq-XSZ Please follow Raphaël Hoogvliets for such an amazing post. #MLOps #ContinuousIntegration #CIPipeline #MachineLearning #GitLabCI #Docker #MLflow #DataScience #DevOps #ModelDevelopment #Python #Automation #SoftwareEngineering #AI #Tech

Get {50%} 𝗢𝗙𝗙 (𝗖𝗼𝗱𝗲 - LLM50) on our 𝗟𝗟𝗠 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽 𝗖𝗼𝘂𝗿𝘀𝗲 - https://lnkd.in/dPJTm5bR ================================================== 🔥 Breaking Down OpenAI's New o1 Models: What You Need to Know ================================================== OpenAI has released their latest AI models - o1-preview and o1-mini - and they're making waves in the AI community. Here's what sets them apart: 📊 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Already leading the pack on LiveBench benchmarks 🧠 𝗨𝗻𝗶𝗾𝘂𝗲 𝗗𝗲𝘀𝗶𝗴𝗻 𝗣𝗵𝗶𝗹𝗼𝘀𝗼𝗽𝗵𝘆: Intentionally slower, prioritizing thorough reasoning Excels at complex tasks: science, coding, and math Built-in chain-of-thought reasoning (though hidden from API) 💰 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗕𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻: o1-preview: $60 per 1M output tokens, $15 per 1M input tokens o1-mini: $12 per 1M output tokens, $3 per 1M input tokens ⚠️ 𝗖𝘂𝗿𝗿𝗲𝗻𝘁 𝗟𝗶𝗺𝗶𝘁𝗮𝘁𝗶𝗼𝗻𝘀: Beta phase - text only No system messages, fixed temperature Limited weekly usage (30 messages for preview, 50 for mini) No streaming, tools, or functions 💡 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: While impressive, for most day-to-day tasks, GPT-4o remains the more practical choice - faster and more cost-effective. 🔍 𝗧𝗲𝗰𝗵 𝗦𝗽𝗲𝗰𝘀: Both models feature a 128k context window o1-preview: 32k max tokens o1-mini: 65k max tokens Follow Shiv Sakhuja for more such amazing posts. #AI #MachineLearning #OpenAI #TechNews #ArtificialIntelligence What are your thoughts on these new models? Have you had a chance to try them out?

Get {50%} 𝗢𝗙𝗙 (𝗖𝗼𝗱𝗲 - LLM50) on our 𝗟𝗟𝗠 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽 𝗖𝗼𝘂𝗿𝘀𝗲 - https://lnkd.in/dPJTm5bR ================================================== 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝗳𝗼𝗿 𝗥𝗔𝗚 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 ================================================== Chunking is a game-changer in optimizing retrieval-augmented generation (RAG) systems! 📚 By breaking down text into smaller, manageable pieces, we can improve both retrieval quality and response accuracy. Here are the six main types of chunking: 1. 𝗦𝗲𝗻𝘁𝗲𝗻𝗰𝗲-𝗟𝗲𝘃𝗲𝗹 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Ideal for factual queries, though it may lose context across multiple sentences. 2. 𝗣𝗮𝗿𝗮𝗴𝗿𝗮𝗽𝗵-𝗟𝗲𝘃𝗲𝗹 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Great for maintaining thematic context, but it may lose granularity. 3. 𝗧𝗼𝗽𝗶𝗰-𝗕𝗮𝘀𝗲𝗱 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Perfect for large-scale document processing but can sometimes cut off important ideas. 4. 𝗙𝗶𝘅𝗲𝗱-𝗦𝗶𝘇𝗲 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Balances coherence and size, crucial for customer service bots or academic research. 5. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁-𝗔𝘄𝗮𝗿𝗲 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Optimizes precision by adapting to the text’s semantic meaning. 6. 𝗛𝘆𝗯𝗿𝗶𝗱 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Combines various chunking strategies to tailor responses to specific use cases. 💡 Whether you're building a knowledge base, answering complex questions, or handling thematic documents, choosing the right chunking strategy is key to success in natural language processing tasks. Follow Bhavishya Pandit for more such amazing content. #AI #NLP #Chunking #MachineLearning #RAG #NaturalLanguageProcessing #LLM #AIResearch #DataScience

50% OFF on 𝗟𝗟𝗠 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽 𝗖𝗼𝘂𝗿𝘀𝗲 : https://lnkd.in/d2NEu4aw ==========================================  Introducing OpenAI Canvas: A New Era for Creative Collaboration ========================================== OpenAI has just launched 𝗖𝗮𝗻𝘃𝗮𝘀, a revolutionary AI tool designed to transform the way we brainstorm, prototype, and collaborate in real-time. Here’s what makes Canvas stand out: ✅ 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Work seamlessly with your team on the same project, with live updates and interactive features. ✅ 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆: Integrated AI helps generate ideas, automate tasks, and refine workflows. ✅ 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀: Whether you're a designer, developer, business strategist, or entrepreneur, Canvas adapts to your workflow – from visualizing ideas to technical prototyping. ✅ 𝗔𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲 𝗳𝗼𝗿 𝗔𝗹𝗹: With a user-friendly interface, both technical and non-technical users can leverage AI to boost creativity and productivity. This is a game-changer for anyone looking to take their brainstorming and project planning to the next level. Explore how Canvas is shaping the future of collaboration! Link to the blog from OpenAI - https://lnkd.in/e2WCphXg #openai #chatgpt #canva

50% OFF on 𝗟𝗟𝗠 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽 𝗖𝗼𝘂𝗿𝘀𝗲 : https://lnkd.in/d2NEu4aw ========================================== 📢 Exploring the Future of AI with 𝗦𝗺𝗮𝗹𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗦𝗟𝗠𝘀) In the evolving world of AI, Small Language Models (SLMs) are gaining traction for their tailored solutions and domain-specific accuracy. These models, trained on niche data, are designed to generate highly relevant and precise outputs, surpassing larger models in targeted contexts. 💡 𝗪𝗵𝘆 𝗦𝗟𝗠𝘀 𝗠𝗮𝘁𝘁𝗲𝗿: Affordable and energy-efficient Easier to customize and deploy Valuable for education and industry-specific applications 🚀 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: Smartphones 📱 Smart home devices 🏠 Wearable tech ⌚ Automotive systems 🚗 Models like Llama-3 8B, Phi-3, and Stable Beluga 7B are leading the charge in this space. Ready to learn more about the rise of SLMs and how they’re shaping the future of AI? Stay tuned! #AI #SmallLanguageModels #GenerativeAI #TechInnovation #FutureOfWork #MachineLearning #ArtificialIntelligence #DataScience

50% OFF on LLM Interview Prep Course : https://lnkd.in/d2NEu4aw ========================================== 🚀 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗚𝗣𝗨 𝗠𝗲𝗺𝗼𝗿𝘆 𝗳𝗼𝗿 𝗦𝗲𝗿𝘃𝗶𝗻𝗴 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠𝘀) As the demand for LLMs like Llama 2 70B grows, one question stands out: "𝗛𝗼𝘄 𝗺𝗮𝗻𝘆 𝗚𝗣𝗨𝘀 𝗱𝗼 𝗜 𝗻𝗲𝗲𝗱?" To answer this, understanding GPU memory requirements is key. Here's a simple formula that helps calculate it: 🧠 Formula: 𝗠 = (32/𝗤)(𝗣 * 4𝗕) * 1.2 Where: 𝗠 = GPU memory in GB 𝗣 = number of model parameters (e.g., Llama 70B has 70 billion) 𝗤 = bits used for loading the model (e.g., 16-bit, 8-bit, or 4-bit) 1.2 = 20% overhead for additional memory needs For example: 𝗟𝗹𝗮𝗺𝗮 2 70𝗕 in 16-𝗯𝗶𝘁 requires 168𝗚𝗕 of memory — meaning 2𝘅 𝗔100 80𝗚𝗕 GPUs are needed. But there's more: Quantization can reduce memory needs without losing much performance. By lowering precision (e.g., 8-bit or 4-bit), memory and computational requirements drop significantly. 🔧 𝗨𝘀𝗶𝗻𝗴 4-𝗯𝗶𝘁 𝗾𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 for Llama 2 70B reduces memory to 42𝗚𝗕, allowing it to run on 2𝘅 𝗟4 24𝗚𝗕 𝗚𝗣𝗨𝘀. If you’re working on deploying LLMs efficiently, quantization is a game-changer. Check out the full article for a deeper dive: https://lnkd.in/dZZ_S6hg #AI #LLM #GPU #MachineLearning #Quantization #Llama2 #DeepLearning