Yair Galili

It was so simple, they couldn’t believe no one figured this out before. Prof. Moran Bercovici and Dr. Valeri Frumkin from the Technion - Israel Institute of Technology developed a new way to create optical lenses using cheap materials and a straightforward process. Here’s how it works: they inject a liquid polymer into a mold that’s submerged in an immersion liquid. By carefully adjusting the immersion liquid's density to match that of the polymer, they eliminate the effects of gravity, allowing the polymer to retain a perfectly smooth and precise shape as it hardens under UV light. The result is a lens with an exceptionally smooth surface—smoother than what traditional polishing can achieve. This method isn’t limited to simple lenses. It can also be used to create complex freeform optics, which are surfaces designed to bend and focus light in precise ways for applications like multifocal glasses, advanced projectors, and even virtual reality systems. These optical components are usually difficult to manufacture, but with this technique, they can be produced much more affordably. #NASA has recognized the potential of this innovation for space exploration. In microgravity, this method could be used to produce large, high-quality optics for telescopes directly in orbit, bypassing the limitations of launching bulky equipment from Earth. This simple yet ingenious Israeli invention opens up new possibilities both on Earth, by providing affordable glasses to those who need them, and in space, by enabling the next generation of telescopes. #IsraeliInnovation #Optics #Space

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𝐏𝐡𝐢𝐝𝐚𝐭𝐚 - 𝐁𝐮𝐢𝐥𝐝 𝐀𝐈 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭𝐬 𝐰𝐢𝐭𝐡 𝐦𝐞𝐦𝐨𝐫𝐲, 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐚𝐧𝐝 𝐭𝐨𝐨𝐥𝐬 Phidata is a framework for building Autonomous Assistants (aka Agents) that have - long-term memory, - contextual knowledge and - ability to take actions using function calling. 𝐔𝐬𝐞 𝐩𝐡𝐢𝐝𝐚𝐭𝐚 𝐭𝐨 𝐭𝐮𝐫𝐧 𝐚𝐧𝐲 𝐋𝐋𝐌 𝐢𝐧𝐭𝐨 𝐚𝐧 𝐀𝐈 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭 𝐭𝐡𝐚𝐭 𝐜𝐚𝐧: - Search the web using DuckDuckGo, Google etc. - Analyze data using SQL, DuckDb, etc. - Conduct research and generate reports. - Answer questions from PDFs, APIs, etc. - Write scripts for movies, books, etc. - Summarize articles, videos, etc. - Perform tasks like sending emails, querying databases, etc. And much more... 𝐖𝐡𝐲 𝐩𝐡𝐢𝐝𝐚𝐭𝐚? 𝐏𝐫𝐨𝐛𝐥𝐞𝐦: We need to turn general-purpose LLMs into specialized assistants for our use-case. 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: Extend LLMs with memory, knowledge and tools: - Memory: Stores chat history in a database and enables LLMs to have long-term conversations. - Knowledge: Stores information in a vector database and provides LLMs with business context. - Tools: Enable LLMs to take actions like pulling data from an API, sending emails or querying a database. Memory & knowledge make LLMs smarter while tools make them autonomous. #aiagents #agents #llms #generativeai

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Love this take (and this question) from Ravid Shwartz Ziv . My personal view is that current frontier AI is so far from the threshold of "Intern-AGI" that it would be difficult to produce a benchmark for it where results were greater than 0. But there are some subcomponents that such an agent would need, that we could probably benchmark on their own. * Complex long-chain instruction following with wrong and missing steps and adversarial perturbations * Formal self validation for task completion, rejection or redirection defining a policy with 99.9 trajectory match with specific groups of humans. IE, a SWE Intern should be able to guess, with 99.9 percent accuracy or better, whether it has completed a required subtask for its meta task, or needs more help * A zero shot understanding of how to navigate complex space layouts and estimate the time cost of actions with very very high precision

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🚀 𝐁𝐢𝐠 𝐍𝐞𝐰𝐬! 🚀 𝐅𝐫𝐞𝐧𝐜𝐡 𝐀𝐈 𝐥𝐚𝐛 𝐊𝐲𝐮𝐭𝐚𝐢 𝐣𝐮𝐬𝐭 𝐫𝐞𝐥𝐞𝐚𝐬𝐞𝐝 𝐌𝐨𝐬𝐡𝐢, 𝐚𝐧 𝐨𝐩𝐞𝐧-𝐬𝐨𝐮𝐫𝐜𝐞 𝐆𝐏𝐓-𝟒𝐨 𝐜𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐨𝐫 𝐭𝐡𝐚𝐭 𝐜𝐚𝐧 𝐥𝐢𝐬𝐭𝐞𝐧, 𝐡𝐞𝐚𝐫, 𝐚𝐧𝐝 𝐬𝐩𝐞𝐚𝐤 😳 🌟 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: 🌟 🌟 𝐑𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐋𝐋𝐌: Moshi operates with virtually no delay (end-to-end latency of 200ms). It even interrupted the speaker a few times! 😅 🌟 𝐎𝐩𝐞𝐧 𝐒𝐨𝐮𝐫𝐜𝐞: Unlike OpenAI's GPT-4o demoed in May, Moshi will be open source. Code, model, and paper to be released soon. 🌟 𝐈𝐧𝐜𝐫𝐞𝐝𝐢𝐛𝐥𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐒𝐩𝐞𝐞𝐝:: Built in just 4 months by a tiny team of 8 people. French engineers are literally insane 🤯 This Voice AI tech will soon be everywhere, revolutionizing the way we interact with technology forever. 🔔 For more fascinating updates, check out linas.substack.com—the only newsletter you need for insights where Finance meets Technology. Perfect for founders, builders, and leaders. #AI #VoiceAI #OpenSource #Innovation #TechNews #Kyutai #Moshi #GPT4

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Happy to share our work on uplift modeling and GNN causal modeling

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PyTorch Profiler is an interesting tool to pinpoint bottlenecks and improve efficiency. Here's how it can help: 𝗪𝗵𝗮𝘁 𝗜𝘀 𝗣𝘆𝗧𝗼𝗿𝗰𝗵 𝗣𝗿𝗼𝗳𝗶𝗹𝗲𝗿? It’s a performance analyzer for your models. You can track: ✅ CPU and GPU activity ✅ Memory usage ✅ Execution time of operations 𝗪𝗵𝘆 𝗨𝘀𝗲 𝗣𝘆𝗧𝗼𝗿𝗰𝗵 𝗣𝗿𝗼𝗳𝗶𝗹𝗲𝗿? 🔺 𝗙𝗮𝘀𝘁𝗲𝗿 𝗠𝗼𝗱𝗲𝗹 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻: Optimize performance and reduce processing time.   🔺 𝗥𝗲𝗱𝘂𝗰𝗲𝗱 𝗠𝗲𝗺𝗼𝗿𝘆 𝗨𝘀𝗮𝗴𝗲: Minimize the memory footprint of your model.   🔺 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗛𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝗨𝘁𝗶𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Make better use of available resources like CPU and GPU.   🔺 𝗜𝗱𝗲𝗮𝗹 𝗳𝗼𝗿 𝗟𝗮𝗿𝗴𝗲-𝗦𝗰𝗮𝗹𝗲 𝗠𝗼𝗱𝗲𝗹𝘀: Especially beneficial for handling complex, resource-intensive tasks.   🔺 𝗣𝗲𝗿𝗳𝗲𝗰𝘁 𝗳𝗼𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀: Crucial for deploying efficient, scalable solutions. The code example analyzes the performance of a simple linear model. It tracks CPU and GPU usage, memory consumption, and tensor shapes. By using the "record_function" decorator, specific parts of the code are labeled for easier analysis. The collected data helps identify inefficiencies, even in basic operations, so developers can make their models run more efficiently. #pytorch #llms #ai #ml #python

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🚀 Excited to share my latest project on Neural Style Transfer! 🖼️ In this project, I leveraged the pre-trained model by Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin, and Jonathon Shlens, titled "Exploring the Structure of a Real-Time, Arbitrary Neural Artistic Stylization Network." I used TensorFlow Hub to load the model, which performs fast artistic style transfer on any arbitrary painting style. This is a huge improvement over earlier approaches, where the original method was slower, although it worked with any painting style. Now, we have the benefit of real-time performance without being limited to a specific set of styles! 🎨✨ here is the link of web app: https://lnkd.in/gQR_naNc #DeepLearning #MachineLearning #AI #NeuralStyleTransfer #TensorFlow #AIArt #ComputerVision #PreTrainedModels #Innovation

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1 תגובה

❓ 𝗪𝗮𝗻𝘁 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗲 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆? 💡𝗖𝗼𝗱𝗲𝘀𝘁𝗿𝗮𝗹 𝗠𝗮𝗺𝗯𝗮! Mistral AI proudly presents Codestral Mamba, a cutting-edge 𝗠𝗮𝗺𝗯𝗮2 language model for 𝗰𝗼𝗱𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻, licensed under Apache 2.0. 𝗜𝗻𝘀𝗽𝗶𝗿𝗲𝗱 𝗯𝘆 𝗖𝗹𝗲𝗼𝗽𝗮𝘁𝗿𝗮, Codestral Mamba offers linear time inference and models sequences of infinite length, ensuring rapid, efficient responses. 𝗞𝗲𝘆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: 1️⃣ Linear time inference for quick responses 2️⃣ Advanced code and reasoning capabilities 3️⃣ Tested on in-context retrieval up to 256k tokens 𝗪𝗵𝘆 𝗖𝗼𝗱𝗲𝘀𝘁𝗿𝗮𝗹 𝗠𝗮𝗺𝗯𝗮 𝗘𝘅𝗰𝗲𝗹𝘀: 📡 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Unlike traditional Transformer models, Codestral Mamba uses a new architecture that ensures quick and accurate responses regardless of input length. 🏆 𝗛𝗶𝗴𝗵 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: With 7 billion parameters, it offers top-tier performance, making it a powerful tool for developers. 𝗘𝗮𝘀𝘆 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁: 🔧 Available via mistral-inference SDK and TensorRT-LLM 🔧 Raw weights downloadable from HuggingFace 🔧 Local inference support coming soon 🎙️ Would you use it? https://lnkd.in/dyEVjnpX #𝗔𝗜 #𝗠𝗶𝘀𝘁𝗮𝗹 #𝗚𝗲𝗻𝗔𝗜 #𝗟𝗟𝗠 #𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲𝗔𝗜

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Let's check out my Phi-3 llm fine tune model Phi-3 Medium : https://lnkd.in/d5gcAPX4 Phi-3 Mini : https://lnkd.in/dy9ArqSS

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The paper "Attention Is All You Need" presents the Transformer model, a novel architecture for sequence transduction based entirely on attention mechanisms. https://lnkd.in/gbe9HSzE Key IDEAS: - Traditional sequence models rely heavily on recurrent networks, limiting parallelization. - The Transformer architecture replaces recurrence with self-attention, improving training times. - Self-attention allows the model to draw connections across the entire input sequence. - The Transformer achieves a BLEU score of 28.4 on the English-to-German translation task. - Processing can occur in parallel, significantly speeding up computation. - Multi-head attention enables the model to focus on different parts of the input simultaneously. - This model can generalize to other tasks beyond translation, such as constituency parsing. - The design simplifies model architecture by removing the need for complex recurrent structures. - Positional encodings are introduced to maintain the order of sequences. - Attention layers improve the model's ability to learn long-range dependencies. - The paper emphasizes the efficiency gains over state-of-the-art models. - Transformers outperform existing methods with considerably less training cost. - The model can be fine-tuned for various language tasks easily. - Experiments showed better performance with increased attention heads up to a point. - Utilization of dropout and label smoothing during training improves performance. - The model architecture allows for easier interpretation of attention distributions. - Attention heads often learn specific tasks, such as syntactic and semantic relations. - The architecture allows adapting to larger input and output sequences efficiently. - Adequate regularization contributes to superior generalization in learning from data. - Ease of implementation on hardware accelerates research and application. Key INSIGHTS: - Replacing recurrence with attention simplifies architecture and allows for efficient model training. - Self-attention is crucial for capturing dependencies across various positions in input sequences. - The Transformer demonstrates that parallel processing can drastically reduce training time and increase efficiency. - The design showcases how innovations in architecture can redefine state-of-the-art results in machine translation. - By employing a consistent embedding across input and output, the model minimizes errors in translation tasks. - Regularization methods like dropout and label smoothing enhance the robustness of predictions. - Understanding various attention heads can provide insights into the inner workings and decision-making of the model.

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I'm an AI Engineer, of course I use Excel :)) I'm an AI Engineer, of course I google "how to import csv in pandas" :)) #ai #tech #pandas #aiengineer #daily

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Today, we delve into a captivating facet of Deep Neural Networks (DNNs) known as "Permutation Invariant Networks," addressing the challenges posed by unordered inputs. This breakthrough holds tremendous promise in domains such as point cloud data analysis, medical image segmentation, and beyond. The paradigm is introduced by architecture known as PointNet, leveraging symmetric functions to surmount the challenges associated with unordered input. Traditionally, ensuring consistent input order has been pivotal for achieving reliable model performance in few scenarios. PointNet discuses three approaches to achieve Permutation Invariant of input order: 1) Sorting Input: One approach entails meticulously sorting the input into a canonical order. By doing so, the network can seamlessly process inputs irrespective of their original ordering, thereby ensuring consistent and invariant representations. 2) RNN-based Approach: Another strategy involves treating the input as a sequential dataset and training a Recurrent Neural Network (RNN). By augmenting the training data with diverse permutations of the input, the model can adeptly learn to handle inputs with varying orders. 3) Symmetric Function: The most fascinating strategy harnesses the power of simple symmetric functions to aggregate information from individual input points. These functions accept multiple vectors as input and yield an output that remains invariant to the order in which the inputs were provided. Classic examples of symmetric binary functions include addition (+) and multiplication (*). Of these three approaches PointNet, shows that permutation invariance can be achieved by approximating a symmetric function with a MLP Network. For more details refer to paper: https://lnkd.in/gTA5JWVe.

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Retrieval-Augmented Generation (RAG) enhances NLP by combining LLMs with external knowledge. But there's a catch—it demands high computation and memory due to long sequence generation during knowledge injection.  💡 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: 𝗥𝗔𝗚𝗖𝗮𝗰𝗵𝗲   RAGCache tackles these bottlenecks with cutting-edge techniques:   - 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗖𝗮𝗰𝗵𝗶𝗻𝗴: Stores intermediate states in a structured knowledge tree, balancing GPU and host memory usage. - 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗥𝗲𝗽𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗣𝗼𝗹𝗶𝗰𝘆: Tailored for LLM inference and RAG retrieval patterns.   - 𝗦𝗲𝗮𝗺𝗹𝗲𝘀𝘀 𝗢𝘃𝗲𝗿𝗹𝗮𝗽: Combines retrieval and inference to minimize latency.  📈 𝗥𝗲𝘀𝘂𝗹𝘁𝘀:   Integrating RAGCache with tools like vLLM and Faiss delivers:   ✅ 𝟰𝘅 𝗙𝗮𝘀𝘁𝗲𝗿 Time to First Token (TTFT).   ✅ 𝟮.𝟭𝘅 𝗧𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁 𝗕𝗼𝗼𝘀𝘁, optimizing latency and computational efficiency.  Link to the research paper - https://lnkd.in/dj-rtsFK #NLP #RAG #LLM #AI #DeepLearning #MachineLearning

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𝐓𝐨𝐫𝐜𝐡𝐜𝐡𝐚𝐭 𝐚𝐧𝐝 𝐒𝐐𝐋𝐢𝐭𝐞-𝐕𝐞𝐜 𝐚𝐫𝐞 𝐢𝐧 𝐭𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐨𝐫𝐜𝐡𝐜𝐡𝐚𝐭 : PyTorch Torchchat is a recently introduced library that simplifies the process of running LLMs locally on a variety of platforms, including laptops, desktops, and mobile devices. 𝐒𝐐𝐋𝐥𝐢𝐭𝐞-𝐕𝐞𝐜 : SQLite-Vec is a groundbreaking SQLite extension that introduces the capability to store and query vector data directly within the database. #github #trending #generativeai #vectordatabases #sqlite #pytorch

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🚀 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗟𝗹𝗮𝗺𝗮 𝟯.𝟮 𝗳𝗿𝗼𝗺 𝗠𝗲𝘁𝗮: 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗔𝗜 𝗼𝗻 𝗘𝗱𝗴𝗲 𝗗𝗲𝘃𝗶𝗰𝗲𝘀! 🌐 Meta's 𝗟𝗹𝗮𝗺𝗮 𝟯.𝟮 represents the next leap in AI technology—a new generation of lightweight vision and text models designed to fit on edge devices, offering more 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗮𝗻𝗱 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗔𝗜 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀. With 𝘀𝗺𝗮𝗹𝗹 𝘁𝗼 𝗺𝗲𝗱𝗶𝘂𝗺-𝘀𝗶𝘇𝗲𝗱 𝘃𝗶𝘀𝗶𝗼𝗻 𝗟𝗟𝗠𝘀 (𝟭𝟭𝗕 𝗮𝗻𝗱 𝟵𝟬𝗕) and 𝗹𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁 𝘁𝗲𝘅𝘁-𝗼𝗻𝗹𝘆 𝗺𝗼𝗱𝗲𝗹𝘀 (𝟭𝗕 𝗮𝗻𝗱 𝟯𝗕), Llama 3.2 excels at on-device processing with a focus on 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 and 𝘀𝘆𝘀𝘁𝗲𝗺-𝗹𝗲𝘃𝗲𝗹 𝘀𝗮𝗳𝗲𝘁𝘆. 🔑 𝗞𝗲𝘆 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀: 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗢𝗻-𝗗𝗲𝘃𝗶𝗰𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Llama 3.2 reduces latency and improves performance, making it ideal for various applications across industries. 𝟭𝟮𝟴𝗞 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗟𝗲𝗻𝗴𝘁𝗵: Captures nuanced relationships in data for deeper insights. 𝗠𝘂𝗹𝘁𝗶𝗹𝗶𝗻𝗴𝘂𝗮𝗹 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀: Supports 𝗲𝗶𝗴𝗵𝘁 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀, including English, Hindi, Spanish, and more. 𝗧𝗿𝗮𝗶𝗻𝗲𝗱 𝗼𝗻 𝟭𝟱 𝗧𝗿𝗶𝗹𝗹𝗶𝗼𝗻 𝗧𝗼𝗸𝗲𝗻𝘀: Built to understand complex language intricacies from public data. 𝗔𝗺𝗮𝘇𝗼𝗻 𝗕𝗲𝗱𝗿𝗼𝗰𝗸 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Access Llama's power via Amazon Bedrock's serverless API for seamless integration into your AI applications without worrying about infrastructure. 🌟 Use Cases: Llama models are designed for a wide array of tasks: 𝗜𝗺𝗮𝗴𝗲 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 and 𝗩𝗶𝘀𝘂𝗮𝗹 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗧𝗲𝘅𝘁 𝗦𝘂𝗺𝗺𝗮𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻, 𝗦𝗲𝗻𝘁𝗶𝗺𝗲𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀, and 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗩𝗶𝘀𝘂𝗮𝗹 𝗤&𝗔 𝗗𝗶𝗮𝗹𝗼𝗴𝘂𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻, 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻, and 𝗖𝗼𝗱𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗩𝗶𝘀𝘂𝗮𝗹 𝗚𝗿𝗼𝘂𝗻𝗱𝗶𝗻𝗴, 𝗜𝗺𝗮𝗴𝗲-𝗧𝗲𝘅𝘁 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹, and much more! With Meta’s 𝗟𝗹𝗮𝗺𝗮 𝟯.𝟮 models (1B, 3B, 11B, 90B), you can handle multi-step, complex AI tasks while ensuring high performance and scalability. 🌍 Whether you're working in AI-powered 𝘃𝗶𝘀𝘂𝗮𝗹 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴, 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴, or 𝗺𝘂𝗹𝘁𝗶-𝗹𝗶𝗻𝗴𝘂𝗮𝗹 𝘁𝗮𝘀𝗸𝘀, Llama 3.2 empowers your next-gen AI solutions with unmatched efficiency and capability. Let’s revolutionize the world of AI together! 🤖💡 #ai #genai #llama3.2 #meta #amazonbedrock #llm #personalizedai

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