Antonio Montano 🪄’s Post

LLM watermarking is a technique that embeds information in the output of the LLM to verify its source, which aims to mitigate the misuse of AI-generated content. However, work by Qi Pang, Shengyuan Hu, Wenting Zheng, and Virginia Smith shows that common design choices and properties in LLM watermarking schemes make the resulting systems surprisingly susceptible to watermark removal or spoofing attacks—leading to fundamental trade-offs in robustness, utility, and usability. https://lnkd.in/e6BSi2gJ

🌟 Exploring the World of Large Language Models (LLMs) 🌟 Getting started with LLMs was no easy feat! 🚀 Initially, the huge computational power needed—like GPUs and CPUs—makes it a difficult task for a beginner to run LLMs. But after some exploration, I discovered alternatives like Kaggle's 🌐 GPU computation and successfully imported and ran a vision LLM from Hugging Face to complete my task. 🖼️🤖 During my journey, I came across open-source AI tools like Ollama, which use quantization to shrink LLMs, making them suitable for local machines. 🖥️✨ Exploring Ollama introduced me to fascinating vision LLMs like LLaVA (13B parameters), Baklava, and LLaMA 3.2 (3B parameters). These models bring incredible potential for vision-based AI projects. 👁️📊 I also dived into libraries like LangChain 🧩 and Streamlit 💻, which are game-changers for building RAG (Retrieval-Augmented Generation) applications. These tools simplify complex workflows and make AI development approachable. 💡 One of my achievement was building an LLM-powered 🤖 wrapper that classifies images from webcams during online tests 🎥. It automatically detects potential cheating behavior—a step towards ensuring integrity in remote assessments ✅ The journey has been challenging yet rewarding, filled with learning, exploration, and creativity. ✨ If you're stepping into the world of LLMs, don't hesitate to explore the amazing resources out there—your next breakthrough might be just one model away! 💪 https://lnkd.in/dZg8qRiK #AI #LLM #MachineLearning #VisionModels #LangChain #Streamlit #Innovation #OpenSource

Best Practices for Deploying LLM Inference, RAG and Fine Tuning Pipelines on K8s   🎯 Key Innovations:   - Advanced management of large language models (LLMs) lifecycle on Kubernetes.   - Use of inference servers for seamless deployment and auto-scaling of models.   - Integration of retrieval-augmented generation (RAG) with embeddings and vector databases.   💡 Notable Features:   - Customized inference pipelines utilizing NVIDIA's Nim operator and KServe.   - Efficient scheduling techniques for GPU resources with dynamic resource allocation.   - Enhanced security through role-based access control (RBAC) and monitoring capabilities.   🛠️ Perfect for:   - AI/ML Engineers deploying models in production.   - Data Scientists involved in fine-tuning and inference tasks.   - DevOps teams managing cloud-native applications on Kubernetes.   ⚡️ Impact:   - Reduced inference latency via effective model caching techniques.   - Improved GPU utilization optimizing resource allocation and scheduling.   - Increased security and manageability of AI pipelines in enterprise settings.   🔍 Preview of the Talk: In this insightful session, Meenakshi Kaushik and Shiva Krishna Merla from NVIDIA share comprehensive best practices for deploying and managing LLM inference pipelines on Kubernetes. They delve into critical challenges such as minimizing inference latency, optimizing GPU usage, and enhancing security measures. Attendees gain actionable insights on building customizable pipelines and leveraging NVIDIA’s technology stack to ensure efficient model management, ultimately leading to significant performance improvements. For more details, check out the full session here: https://lnkd.in/gRK7zPTM

The video of my talk on our LLM model at CSAIL MIT - Hari Balakrishnan was the host. This is joint work with Siddhartha Dalal It is a very different look at LLMs, developed from first principles - it might change your mind on (or help understand) how these large language models work. I go through the development of a new Domain Specific Language for AskCricinfo where gpt3 learnt our DSL in real-time (and where we accidentally invented RAG+in context learning back in Fall of 2020). The system has been running in production since September 2021 at ESPNCricInfo. We show how Bayesian learning happens with the basic output of an LLM - the next token probability distribution.

In the rapidly evolving world of #ai , the power and potential of Large Language Models (#llms) are undeniable. However, as these models grow in complexity, so do their demands on compute power and memory. This is where quantization becomes a game-changer! 🛠️ Quantization significantly reduces the hardware requirements for running these sophisticated models, making LLMs more accessible and efficient, especially for applications requiring real-time inference on limited resources. I'm excited to share my latest video tutorial https://lnkd.in/gft4KVjk where I dive into the world of #llm quantization using the innovative llama.cpp tool. 🎥 This powerful utility simplifies the conversion of any LLM to the GGUF format, enabling seamless inference on both CPUs and consumer GPUs. I've taken a hands-on approach to demonstrate the entire process in Google Colab, from model quantization to deploying the optimized model on #huggingface. This breakthrough not only democratizes access to cutting-edge #genai technologies but also opens up a plethora of opportunities for developers and businesses alike. #linkedin #tech #opensourceai #llamacpp #qwen #aianytime

NVIDIA's Expertise in Large Language Model Engineering https://lnkd.in/dQi7xitT This document is based on an NVIDIA 's workshop providing an insightful look into AI-driven communication and analysis. With a focus on advanced LLM architectures and prompt engineering, the event showcased cutting-edge techniques to optimize data processing and response generation. Key takeaways from the session included the utilization of retrieval-augmented generation for efficient data sourcing and the significance of embedding vectors for semantic understanding. NVIDIA's practical demonstrations, such as email triage for a fictitious company, exemplified how LLMs can streamline customer service operations by intelligently categorizing and responding to queries. #AI #LLMs #MachineLearning #DataScience #ArtificialIntelligence #Innovation

What we’re building 🏗️, shipping 🚢, and sharing 🚀 this week: FlashAttention! 🧮 Do you know how Large Language Models calculate attention? ⚡ Do you understand how FlashAttention reduces memory and improves computation speed? 2️⃣ What about FA2 or Flash Attention 3? The micro-optimizations done on attention computations can have massive impacts on inference and training speeds. As FA2 has emerged as a new standard, we figured all of this is very much worth diving into for practitioners at the LLM Edge. Join us live tomorrow to demystify the mechanics together: https://lnkd.in/g4xT_MaZ #AIEngineering #Transformers #FlashAttention

The most popular large language models (LLMs) today can reach tens to hundreds of billions of parameters in size and, depending on the use case, may require ingesting long inputs (or contexts), which can also add expense. NVIDIA blog #llm #education #generativeai #conversationalai #sciense #computing #nvidia #NeMo #transformers #tensorrt #telecomunications #deeplearning #machinelearning #consumerinternet

📘 Configuring and Using the LLM Prompt Component in SmythOS: A Complete Guide The LLM Prompt component in SmythOS is revolutionizing how we generate content through AI. Here's a comprehensive breakdown of its capabilities: 🔧 Model Configuration • Default Models: Full OpenAI suite (GPT 3.5, GPT 4) • Custom Models: Seamless integration with Together AI and Claude AI • API Integration: Bring your own keys for maximum flexibility ⚙️ Prompt Settings & Controls • Dynamic prompt configuration with input variables • Temperature control (default: 1) • Top P settings for response breadth • Maximum output tokens customization • Stop sequence definition • Frequency and presence penalties for reduced repetition 🚀 Advanced Customization Options Create custom models with: • Amazon's Bedrock • Google's Vertex AI • Full machine learning feature customization • Credential management options 💡 Practical Implementation Example: Generating Personalized Emails: 1. Configure name and email inputs 2. Set up detailed Sales department prompts 3. Utilize debug mode for JSON output review 4. Implement expressions for content sectioning 🔗 Essential Resources: Documentation: https://lnkd.in/eu2SvfNH Training: https://lnkd.in/eCehmk4K Community Support: Join our Discord at discord.gg/smythos For developers seeking robust language modeling integration, the LLM Prompt component offers unparalleled configurability and extensive customization support.

Introducing our recent paper MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation. This offers a tuning-free module that works with only a single reference image and outperforms existing methods in generating images with high detail fidelity, enhanced identity-preservation and prompt faithfulness https://lnkd.in/eKdEnj6f