Momal Ijaz

Completed "Multimodal RAG: Chat with Videos" course by Intel, Gained proficiency in: • Multimodal Retrieval Augmented Generation (RAG) system architecture • Generating and utilizing multimodal embeddings • Video preprocessing techniques for RAG applications • Implementing multimodal retrieval from vector stores • Leveraging Large Vision-Language Models (LVLMs) • Applying Multimodal Langchain for advanced RAG systems This course has equipped me with cutting-edge skills to build innovative AI solutions that seamlessly integrate video and text data, enhancing my capabilities in developing next-generation AI applications. thanks to DeepLearning.AI for offering such an amazing course!

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📌LLaMA-Omni: Seamless Speech Interaction with Large Language Models Models like GPT-4o enable real-time interaction with large language models (LLMs) through speech, significantly enhancing user experience compared to traditional text-based interaction. However, there is still a lack of exploration on how to build speech interaction models based on open-source LLMs. 🔗https://lnkd.in/dchmdne2 #LargeLanguageModels

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Learn how you can convert a PyTorch model, trained on the web, to a mobile-optimized format. Madhur Zanwar describes the process using a few lines of code. He also discusses his journey and how one approach didn't yield satisfactory results while trying to run it in a React Native app. #machinelearning #pytorch #computervision #edgeml #reactnative #objectdetection #yolov5

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Anthropic's new methods for contextual retrieval are reshaping how we interact with documents. By parsing complex data structures and using advanced ranking mechanisms, they’re improving the quality of information retrieved. Learn more from Umair Ali Khan now. #GenAI #LLM

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Check out this detailed tutorial written by Tahreem Rasul on how to build a RAG chatbot with Chainlit, LangChain and Literal AI. You will learn how to: 1) Develop a RAG pipeline to process and retrieve the most relevant PDF documents from the arXiv API. 2) Develop a Chainlit Copilot that interacts with the web page to display results from the online retrieval. 3) Improve the app with LLM observability features from Literal AI. https://lnkd.in/ejkeep2K #OpenAI #ChatGPT #LLMOps

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If you want to learn to reduce the chances of Out-of-Memory (OOM) errors when doing training on the GPU see this talk from Jane Xu at Meta at the PyTorch Conference 2024 (link in comments). She shares insights into techniques such as:  - Fully Sharded Data Parallel 1 & 2 - Split your layers across GPUs while increasing batch size. One approach is that all layers should be homogeneous on splitting and the other not. - Activation Checkpointing - An activation is any intermediary state from the output of some math. This is typically needed for future calculations, so you can store them or you can recompute them if your compute is fast. You have to communicate across GPUs to reconcile layers. - CPU Offloading - Move your activations to CPU memory to create more space for training data and other layers on the GPU using CUDA Streams. The diagrams are easy to understand and the memory profiles are useful to digest and pattern match to your own training. #machinelearning #datascience #llm

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Learn how to fine-tune Seq2Seq models using Kaggle's free GPUs and flaunt your work on Hugging Face Hub. Our ML engineer Abdul Rehman Raja's latest article is the ultimate walkthrough. Link: https://lnkd.in/d_qHm_3x

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💡 Optimizing your AI's retrieval process can drastically reduce errors and boost performance. In this article, Atita Arora details advanced strategies for optimizing RAG systems through an evaluation-based methodology. Here are some of the insights we had using Qdrant and Quotient AI: ✅ Low chunk relevance and context relevance make the model more likely to hallucinate. ✅ Retrieving more documents and reducing chunk size boosts model performance. ✅ Dynamic retrieval adapts to query variations, improving response accuracy. ✅ Changing LLMs or prompts affects response quality, requiring fine-tuning for better performance. Follow along with her four-step experiment and try a pipeline in various ways: https://buff.ly/3zaTnVG Github Notebook: https://lnkd.in/dbKGgFTT Workshop on YouTube: https://lnkd.in/dqGWkqhm

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Quotient AI helps developers measure the quality of their RAG systems - so they know how to improve them. Read the latest case study with Qdrant 👇

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💡 New Tutorial 💡 Learn how to auto-annotate your image datasets with a single click, regardless of their size, using the Clarifai Platform. The platform now supports the Auto Annotation feature, allowing you to leverage machine learning models and workflows from the community to automatically annotate your data. Check out the full tutorial here: https://lnkd.in/gsj4JdQ7

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🤗 With Hugging Face's incredible Accelerate library, you can add support for distributed training to your pipelines in just a few lines of Python. And with the Comet + Accelerate integration, managing your distributed experiments is just as easy. You can track metrics, version datasets, tune hyperparameters, and more across multiple GPUs with just a few lines of code. 👉 Check out our quick-start Colab to see the integration in action: https://lnkd.in/dguVZ785

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📝 A Curated Set of NLP Primers: Attention, Autoregressive vs. Autoencoder Models, Transformers, BERT 🔹 Attention: http://attention.aman.ai - Overview (The Attention Mechanism, The Bottleneck Problem, The Context Vector Bottleneck, How Attention Solves the Bottleneck Problem) - Origins of Attention - Attention: Under the Hood - Classic Sequence-to-Sequence Model - Sequence-to-Sequence Model with Attention - Context Vector - Compared: Attention Mechanism v/s Fixed-length Context Vector Approach - Extensions to the Classic Attention Mechanism - Self-Attention (Why Have Multiple Attention Layers?) - Comparative Analysis: Additive vs. Scaled Dot-Product Attention (Computational Efficiency, Theoretical Complexity, Usage and Performance, Implementation Details) - Multi-head Attention - Cross Attention - Ghost Attention - Linear Attention 🔹 Autoregressive vs. Autoencoder Models: http://autoregenc.aman.ai - How do Decoder Models Work? - How do Encoder Models Work? - Encoder-Decoder Models - XLNet: the Best of Both Worlds (Permutation Language Modeling) 🔹 Transformers: http://transformer.aman.ai - Mathematical background (Vectors, Matrix Multiplication, Dot Product, Masking, Sampling) - Attention (Additive/Multiplicative Attention, Self/Cross-Attention, Multi-Head Attention) - Core components of the Transformer Architecture (Embeddings, Positional Encoding, Skip Connections, Layer Normalization, Softmax) - Top-level Transformer Architecture (Encoder and Decoder stack) - Implementation details (Byte-Pair Encoding, Teacher Forcing, Label Smoothing) - Lessons learned (What are Transformers learning? Why is training them so hard?) - Pros/cons of Transformers relative to CNNs/RNNs - Relation between Transformers and Graph Neural Networks 🔹 BERT: http://bert.aman.ai - Background: Pre-Training - Contextualized Embeddings - Masked Language Modeling (MLM) - Next Sentence Prediction (NSP) - BERT’s Encoder Architecture vs. Other Decoder Architectures - The Strength of Bidirectionality - Supervised Finetuning #artificialintelligence #deeplearning #nlp

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Are you struggling with coding or math problems, unable to find accurate solutions for even the most complex issues? Look no further, because AIcraftar is here for you. Log in to AIcraftar and use our multi-model tools to solve any problem you encounter. Get answers to all your questions using GPT-4.0, or switch to the Anthropic Cloud or AIcraftar's Tharo G model, which reads your previous conversations to provide even better responses. AIcraftar is a multi-AI LLM conversational tool. If you need to search for something, you can use our advanced search tool. Additionally, we offer a translator, dictionary, writing prompt templates, AI voice generator, and all premium models for image generation. Why wait any longer? Log in to AIcraftar now and make your life easier with technology. For any inquiries, contact us via email at [email protected]. Visit our website: aicraftar.com. #AIcraftar #TechSolutions #ArtificialIntelligence #MachineLearning #CodingSupport #MathSolutions #AdvancedAI #GPT4 #AnthropicCloud #TharoG #MultiModelAI #AIIInnovation #TechTools #Productivity #AIIntegrations #TechnologySolutions #AIInBusiness #FutureOfWork #AITools #DigitalTransformation

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Explore fine-tuning the new Llama 3 with TorchTune and HuggingFace. Find out which tool enhances your ML projects best. Read here → https://bit.ly/44g62T6

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🚀 Unlocking AI with Vector Databases & RAG 🌐 🔹 Vector Databases: A cutting-edge solution for efficient data retrieval, storing data as vectors for fast, accurate searches. 🔹 Embeddings & Dimensions: Transform data points into vectors with multiple dimensions, capturing rich semantic features for nuanced comparisons. 🔹 Similarity Metrics: Utilize cosine similarity (or alternatives like Euclidean distance and dot product) to measure how closely data points align. 🔹 Retrieval-Augmented Generation (RAG): Boost AI performance by retrieving relevant context before generating responses, leading to smarter, context-aware interactions. 🔹 LLMs (Large Language Models): Powerful AI models that understand and generate human-like text, driving innovation in natural language processing and beyond. 💡 As AI continues to evolve, vector databases and RAG are crucial for extracting valuable insights from massive datasets. The future of AI is here, and it’s exciting! Feel free to play - https://lnkd.in/gSiaUJQG #AI #MachineLearning #VectorDatabases #Embeddings #RAG #LLMs #NLP #DataScience #Innovation #Tech

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