Swarmauri的动态

?? Master Swarmauri’s Message Classes for Conversational AI! ???? Dive into the foundation of conversational AI with Swarmauri’s Message Classes! This guide explains how to effectively use HumanMessage, AgentMessage, FunctionMessage, and SystemMessage to build dynamic, context-aware conversations. Whether you're creating chatbots or AI-powered tools, these classes are your essential building blocks for managing interactions. ?? Explore the notebook here: https://lnkd.in/gPj8kTbZ ????? Connect and Learn More: ◆ GitHub Profile: https://lnkd.in/e8qgrG7Q ◆ GitHub Repository: https://lnkd.in/gPj8kTbZ ◆ Join the Community: https://lnkd.in/e8NZEtcw ◆ Watch Tutorials: https://lnkd.in/gSny7Fr4 ?? Start building smarter conversations today! Swarmauri empowers you to create robust and engaging AI-powered interactions. Let’s bring your ideas to life! ???? #AI #MachineLearning #Swarmauri #OpenSource #ConversationalAI #Python #Programming #Freshers #Engineering #Coding

Looking to integrate tools and manage complex tasks effortlessly? Our Jupyter Notebook demonstrates how to use Swarmauri Tool Models and ToolAgents to combine tools, conversations, and LLMs for efficient AI workflows. ?? Explore the notebook here: https://lnkd.in/gc8W4YEQ ?? What You'll Learn: ◆ Tool Models Overview: Understand classes like OpenAIToolModel, AnthropicToolModel, and more to leverage specific LLM providers effectively. ◆ Creating a ToolAgent: Combine tools like CalculatorTool with conversations and LLMs to achieve real-time functionality. ◆ Toolkit Management: Add, manage, and utilize tools dynamically for enhanced flexibility and scalability. ◆ Hands-On Examples: Learn to automate tasks like performing calculations and interacting with toolkits seamlessly. ?? Connect and Learn More: ?? GitHub Profile: https://lnkd.in/e8qgrG7Q ?? GitHub Repository: https://lnkd.in/gPj8kTbZ ?? Join the Community: https://lnkd.in/e8NZEtcw ?? Watch Tutorials: https://lnkd.in/gSny7Fr4 ?? Unlock smarter workflows today! Swarmauri Tool Models and ToolAgents simplify complex AI development processes, letting you focus on innovation. Let’s build something impactful together! ???? #ArtificialIntelligence #Swarmauri #OpenSource #Python #AiTools #Freshers #Productivity #Startups #Futurism #Careers #Programming #Coding #Research

Want to supercharge your AI applications? Explore how Swarmauri SDK Tools and Toolkits can extend the capabilities of large language models (LLMs). With this Jupyter Notebook, learn to integrate and manage tools for API calls, code execution, weather data retrieval, and much more. The Toolkit class makes it easy to organize, manage, and retrieve tools dynamically for seamless AI development. ?? Explore the notebook here: https://lnkd.in/gHCphfSF ?? What You'll Learn: ◆ What Are Tools? Enhance LLMs with functionality like calculators, APIs, databases, and custom workflows. ◆ Prebuilt Tools in Swarmauri: Includes RequestTool, CodeExtractorTool, WeatherTool, and others for real-world use cases. ◆ Toolkit Class: Discover how to add, manage, and retrieve tools efficiently for versatile AI applications. ◆ Hands-On Examples: Learn to perform tasks like arithmetic calculations, weather retrieval, and API integration step by step. ?? Connect and Learn More: ?? GitHub Profile: https://lnkd.in/e8qgrG7Q ?? GitHub Repository: https://lnkd.in/gPj8kTbZ ?? Join the Community: https://lnkd.in/e8NZEtcw ?? Watch Tutorials: https://lnkd.in/gSny7Fr4 ?? Empower your AI projects today! With Swarmauri SDK, building powerful, intelligent systems has never been simpler. Let’s create impactful solutions together. ???? #ArtificialIntelligence #Research #Swarmauri #PythonProgramming #AITools #Developer #SoftwareEngineer #OpenSource #Learning #Productivity #Robotics #RPA #Futurism

?? ?????????????????? ?????????? ???? ?????? ???????? ???????????????????? ???????? ???????????? ???? I’m excited to share the progress I’ve made in my ??????????-?????? ?????? ??????????????, where I’ve developed a Python-based solution that uses ??????????’?? ???????????????? ???????????? ?????? to extract text from images! As part of my hands-on learning journey, I’m diving deep into ?????????????? ?????????????????? ?????????????????????? (??????), a powerful AI technology that allows machines to read and interpret text from scanned images, photos, and even handwritten notes. ?????????? ?????? ?????? ????????- https://lnkd.in/dtpq8hEN #Azure #OCR #MachineLearning #Python #ComputerVision #CloudComputing #ArtificialIntelligence #TechInnovation #AzureAI ICT Academy Infosys

???Excited to Share My Latest Project: Handwritten Digit Recognition System for Real-Time Prediction!?????? I'm thrilled to announce the completion of my latest project where I developed a?real-time handwritten digit recognition system?using deep learning techniques. This project uses a Convolutional Neural Network (CNN) to accurately detect handwritten digits from images in real-time. ???Key Highlights of the Project: Real-time digit recognition?from images captured via webcam or uploaded photos High accuracy and performance with?over 99% accuracy Built using?TensorFlow?and?Keras?for model training and prediction Practical use cases include?digit recognition for various applications?such as form filling, postal address reading, and more! ???Check out the code and details on my GitHub: [https://lnkd.in/gdeC8pp6] I’m excited to continue exploring the potential of computer vision and machine learning for solving real-world problems. Feel free to check out the repository, and I would love to hear any feedback! #MachineLearning #AI #DeepLearning #ComputerVision #HandwrittenDigitRecognition #TensorFlow #Keras #Python #ArtificialIntelligence #RealTimePrediction #TechInnovation

?? Thrilled to share my recent machine learning project: Cat vs Dog Image Classification! ???? As a Data Science Intern at Bharat Intern, I developed a Convolutional Neural Network (CNN) to classify images of cats and dogs. The project uses Python, TensorFlow/Keras, and Google Colab, and was trained on the Dogs vs. Cats dataset from Kaggle, with 20,000 training images and 5,000 validation images ?? Explore the project here: https://lnkd.in/ewZ2yn_6 Key highlights of the project: Implemented a CNN architecture with Conv2D, MaxPooling2D, and Dropout layers. Visualized model performance and data distribution using Matplotlib. Improved my deep learning and computer vision skills through hands-on experience. I’m grateful for the opportunity at Bharat Intern and excited to connect with others in the field to discuss the potential applications of CNNs in image classification! #MachineLearning #DeepLearning #CNN #Python #Kaggle #GoogleColab #DataScience #TensorFlow #Internship #BharatIntern

Hey everyone! I’ve been diving into various machine learning algorithms recently and came across an interesting technique called SMOTE (Synthetic Minority Over-sampling Technique). ?? SMOTE is a powerful preprocessing method used to tackle class imbalance in datasets, making it easier to train more accurate models. For this project, I’ve applied SMOTE on data related to direct marketing campaigns and compared the performance of classifiers with and without SMOTE. ?? I’d love to get your thoughts on the code and any suggestions you might have to improve the results. Let’s connect and collaborate! ?? #MachineLearning #DataScience #SMOTE #ClassImbalance #DataPreprocessing #ArtificialIntelligence #MarketingAnalytics #DataAnalysis #Python #AI #TechInnovation #ML #Analytics #DataDriven #DataScienceCommunity #KrishnaikMachineLearning #DataScienceWithKrishnaIk #Krishnaik https://lnkd.in/d2PptZTx

?? Implementing LoRA (Low-Rank Adaptation) from Scratch in PyTorch! ?? ?? Part 1 of the series "Breaking Down LoRA" ?? For months, I’ve been fine-tuning LLMs like the Llama herd of models ??, Mixtral, Codestral, and more. But this time, I wanted to dig deeper into the mechanics. So, I stepped back from higher-level tools and dove straight into PyTorch, building a LoRA (Low-Rank Adaptation) implementation from scratch. GitHub Repo: https://lnkd.in/dhwFv9zp This exercise wasn’t just about learning the basics—I’ve been immersed in fine-tuning workflows for a while now. It was about bridging the gap between theory and hands-on implementation, while also sharpening my PyTorch skills. Taking design inspiration from Hugging Face, here’s what I worked on: 1?? LoRA Adapter for PyTorch’s Linear Layer: When enabled, inference happens through ?? + (α/??)?Δ??; otherwise, the original model weights are used. Code: https://lnkd.in/dub47uBj 2?? Wrapper for PyTorch Models: This wrapper replaces target layers with LoRA-adapted ones based on user configuration. You can enable/disable all LoRA layers at once or merge the final weights into the original model before saving. Code: https://lnkd.in/dmwVRXvm ? To validate the implementation, I ran a series of fine-tuning experiments: 1?? Pre-training: I first pre-trained a multi-layer perceptron (MLP) model on the classic (flattened) MNIST dataset. Code: https://lnkd.in/d_cmg4iZ 2?? Full Fine-Tuning: Then, I ran full fine-tuning on the (flattened) FashionMNIST dataset to see how well the pre-trained model adapted. Code: https://lnkd.in/dGC5vfGw 3?? LoRA-based Fine-Tuning: Finally, I applied LoRA-based fine-tuning on the (flattened) FashionMNIST dataset. Code: https://lnkd.in/dt5Bf5VM ?? The Results? ??The LoRA fine-tuned model achieved ~99% accuracy of the full fine-tuned model with <3.5% of the trainable parameters. This project has been a deep dive into understanding LoRA beyond the libraries—right into the nuts and bolts of how it really works. It’s not an official implementation, but it’s been a fantastic learning experience. ?? Feel free to clone the repo, tweak the config like target modules, rank, alpha, etc., and experiment! I’d love to see what you come up with. Let’s keep learning and building together! P.S., more interesting experiments to follow ?? #MachineLearning #PyTorch #LoRA #FineTuning #AI #DeepLearning #LLMs #ML #Experimentation

?? ?????????????????? ???? ???????? ?????????? ????????????: ?????? ?? ?????????????? ?? Excited to share my experience testing a powerful Azure OpenAI model program as part of Lab 9 with ICT Academy. This hands-on activity allowed me to develop and interact with an AI assistant that’s designed to: ? Understand user prompts dynamically ? Recommend personalized suggestions (like hikes near Rainier National Park!) ? Leverage the capabilities of ?????????? ????????????'?? ???????? ?????????????????????? to deliver engaging and relevant results ?? ?????? ??????????????????: Enhanced my knowledge of integrating Azure AI services into Python applications. Learned to structure effective system messages for more accurate AI responses. Strengthened practical skills in working with environment variables and APIs. This program is a perfect example of how AI can be tailored to deliver highly specific solutions in real-world scenarios! ? A big shoutout to ?????? ?????????????? for creating such engaging labs that bridge theory with practical implementation. ?? Checkout the code - https://lnkd.in/eDDY6gkr #AzureOpenAI #ICTAcademy #AI #CloudComputing #AIApplications #AzureLabs #ContinuousLearning ICT Academy Infosys

?? Task 04: Analyze and Visualize Sentiment Patterns in Social Media Data ?? As part of my Data Science Internship at Prodigy InfoTech, I successfully completed Task 04, where I analyzed and visualized sentiment patterns in social media data to understand public opinion and attitudes towards specific topics or brands. ?? ?? Key Highlights: Cleaned and preprocessed the data for accurate analysis ?? Visualized the distribution of sentiments (Positive, Negative, Neutral, Irrelevant) ?? Generated word clouds to identify the most common words associated with each sentiment ?? Analyzed text length distributions and common words by sentiment category ?? Explored sentence and character count distributions ?? Github Link : https://lnkd.in/gh3B4DEq Thank you to Prodigy InfoTech for this amazing learning opportunity! ?? #ProdigyInfoTech #DataScience #Internship #SentimentAnalysis #Python #Visualization #NLP #Kaggle #DataVisualization #TextAnalysis #NaturalLanguageProcessing