NimbleEdge的动态

Mistral AI Brings Large Language Models to Your Pocket ???? Hey AI enthusiasts! Exciting news from Mistral as the French AI startup introduces "Les Ministraux," a family of AI models designed for everyday devices: ? Two models: Ministral 3B and Ministral 8B ? Features a 128,000 token context window ? Outperforms models from Google, Microsoft, and Meta ? Use cases: on-device translation, offline smart assistants, local analytics, and robotics applications The standout: ? Ministral 8B available for download (research only) ? Commercial use requires direct contact with Mistral ? Cloud access via "La Platforme" and partner clouds ? Pricing: 4-10 cents per million tokens This shift towards device-friendly models raises intriguing questions: 1. Impact of on-device AI on daily tech interactions 2. Privacy implications of local powerful AI models 3. Will this democratize AI development or pose access limitations? To AI developers: How will these models integrate into your projects? And to policy experts: Does this edge AI trend necessitate new regulatory approaches? Let's engage and explore together! Your insights shape our understanding of the evolving AI landscape. #AIInnovation #EdgeComputing #Mistral #TechTrends Read more: [TechCrunch Article](https://lnkd.in/du8etAW8)

The days when AI relied heavily on Large Language Models (LLMs) are behind us. The conversation has now shifted to Small Language Models (SLMs) that can operate efficiently on mobile devices, laptops, or on-premise systems. These models consume less internet bandwidth, support a wide range of languages, and can be fine-tuned for specific tasks with ease. SLMs represent the future of AI – more accessible, cost-effective, and adaptable to real-world applications. #generativeAI #SLM

French AI startup Mistral AI just?launched?two new compact language models designed to bring powerful AI capabilities to edge devices like phones and laptops. ?? The new ‘Les Ministraux’ family includes Ministral 3B and Ministral 8B models, which have just 3B and 8B parameters, respectively. ?? Despite their small size, the models outperform competitors like Gemma and Llama on benchmarks, including Mistral's 7B model from last year. ?? Minstral 8B uses a new ‘interleaved sliding-window attention’ mechanism to efficiently process long sequences. ?? The models are designed for on-device use cases like local translation, offline assistants, and autonomous robotics. While we await the incoming rollout of Apple Intelligence as many users’ first on-device AI experience, smaller models that can run efficiently and locally on phones and computers continue to level up. Having a top-tier LLM in the palm of your hand is about to become a norm, not a luxury. #ai #technology

Implementing Small Language Models (SLMs) with RAG on Embedded Devices Leading to Cost Reduction, Data Privacy, and Offline Use https://lnkd.in/d_rv2H7X At deepsense.ai, we have developed a groundbreaking solution that combines Advanced Retrieval-Augmented Generation (RAG) with Small Language Models (SLMs) to enhance the capabilities of embedded devices. SLMs, with 3 billion parameters or less, are smaller, faster, and more lightweight than traditional language models. By implementing SLMs directly on edge devices, businesses can benefit from cost reduction, improved data privacy, and offline functionality. This means significant savings by eliminating the need for cloud inference, seamless offline use, and local processing for enhanced data privacy. Our ongoing research initiatives are focused on further improving SLMs, including better hardware utilization, 1-bit LLMs for memory and inference speed benefits, mixtures of experts, and sparse kernels with pruning. We have also developed a complete RAG pipeline with SLMs capable of running on resource-constrained Android devices, addressing challenges such as memory limitations, platform independence, and the maturity of inference engines. Our tech stack includes llama.cpp for SLM inference, bert.cpp for model embedding, Faiss for efficient search, Conan for package management, and Ragas for automated RAG evaluation. For more information and free consultation, visit our AI Lab in Telegram @itinai or follow us on Twitter @itinaicom. #productmanagement #ai #ainews #llm #ml #startup #innovation #uxproduct #artificialintelligence #machinelearning #technology #ux #datascience #deeplearning #tech #robotics #aimarketing #bigdata #computerscience #aibusiness #automation #aitransformation

Llama 3.2: AI Gets Smarter and Goes Mobile Imagine having a super-smart helper that can understand both words and pictures, right in your pocket. That's what Meta (the company behind Facebook) is bringing us with their new Llama 3.2 AI models. What's New? 1. AI That Sees: The bigger Llama 3.2 models (11B and 90B) can now understand images. It's like having a friend who can look at a photo and tell you all about it or answer questions based on what they see. 2. AI in Your Phone: Smaller versions (1B and 3B) are designed to work directly on your phone or other small devices. This means you can have powerful AI help without needing to connect to the internet all the time. Why Does This Matter? Privacy First When AI runs on your device, your data stays with you. It's like having a personal assistant who never leaves your house with your information. Faster Responses No need to wait for answers from a distant server. It's like having a genius buddy always ready to chat, right in your pocket. New Possibilities - Smart Cameras: Your phone could instantly tell you about objects it sees or help visually impaired users navigate. - Personal Tutors: Imagine having a study buddy that can explain complex topics, available 24/7 on your tablet. - Language Learning: An AI that can see objects and describe them in the language you're learning, right on your phone. What's Next? As this technology becomes more common, we might see: - Smarter home devices that truly understand what we say and see - Educational tools that adapt to each student's needs in real-time - Accessibility features that make technology more inclusive for everyone Llama 3.2 is a big step towards making AI more personal, private, and powerful. It's not just for tech experts anymore – it's AI that's ready to help everyone, right where they are. What do you think? How would you use an AI assistant that can see and understand, right on your phone? #GenAI https://lnkd.in/gTCE8rsa

?? Revolutionizing Customer Support with the Nvidia Nemotron - RLHF ???? The future of customer service is here, powered by AI models like Nvidia Nemotron 70B. What sets this model apart? Its use of Reinforcement Learning from Human Feedback (RLHF), which makes it smarter and more adaptable with every interaction. ???? With Nemotron 70B, I built a customer service chatbot capable of handling complex queries across different. Here’s why it stands out: ?? Human Feedback-Driven: RLHF ensures the chatbot continuously improves, delivering responses that are contextually relevant and helpful. ?? Real-world Learning: The HelpSteer2 dataset trains the model on real customer interactions, enabling it to respond effectively across industries. ?? Cross-domain Applications: Whether it’s managing technical support in telecom, providing financial advice, or assisting with e-commerce transactions, the model delivers tailored, intelligent responses. Here’s how different industries can benefit: ?? E-commerce: Personalized recommendations and order management. ?? Healthcare: Guiding patients with empathy through appointments and inquiries. ?? Finance: Assisting customers with transactions and regulatory compliance. This combination of advanced AI and human feedback is transforming how businesses approach customer service, making it faster, more accurate, and scalable. ???? Excited to see how RLHF-based models like Nvidia Nemotron will shape the future of customer service? Check out my full blog post for more details ?? https://lnkd.in/gFgMT-Zg #AI #CustomerService #NvidiaNemotron #RLHF #LLM #AIApplications ???

NVIDIA Introduces RankRAG: A Novel RAG Framework that Instruction-Tunes a Single LLM for the Dual Purposes of Top-k Context Ranking and Answer Generation in RAG https://lnkd.in/d7eAcXuC Practical Solutions for Retrieval-Augmented Generation (RAG) Challenges in Current RAG Pipeline RAG is facing challenges in efficiently processing large amounts of information and ensuring that it can accurately retrieve relevant content when needed. Advancements in RAG Systems Researchers have developed RankRAG, an innovative framework that enhances the capabilities of large language models (LLMs) in RAG tasks. This approach fine-tunes a single LLM to handle both ranking contexts and generating answers within the RAG framework. RankRAG’s Performance RankRAG has shown superior performance in retrieval-augmented generation tasks across various benchmarks, surpassing existing RAG models and expert ranking systems. Value of RankRAG RankRAG is a significant advancement in RAG systems, providing a unified solution for improving RAG performance across diverse domains. AI Solutions for Business Learn how AI can transform your workflow, identify areas for automation, define KPIs, choose an AI solution, and gradually implement AI to drive business success. AI KPI Management Contact us at [email protected] for advice on managing AI KPIs and continuous insights on leveraging AI. AI for Sales Processes and Customer Engagement Explore AI solutions at itinai.com to enhance your sales processes and improve customer engagement. #RAG #RankRAG #AI #RetrievalGeneration #BusinessSolutions #productmanagement #ai #ainews #llm #ml #startup #innovation #uxproduct #artificialintelligence #machinelearning #technology #ux #datascience #deeplearning #tech #robotics #aimarketing #bigdata #computerscience #aibusiness #automation #aitransformation

Revolutionizing Adapter Techniques: Qualcomm AI’s Sparse High Rank Adapters (SHiRA) for Efficient and Rapid Deployment in Large Language Models https://lnkd.in/dCU6YUEc Revolutionizing Adapter Techniques: Qualcomm AI has developed Sparse High Rank Adapters (SHiRA) to address the challenge of efficiently deploying large language models (LLMs) and latent variable models (LVMs). The traditional methods like Low Rank Adaptation (LoRA) either result in slow adapter switching or significant latency. Practical Solution: SHiRA offers a highly sparse adapter solution that modifies only 1-2% of the base model’s weights, enabling rapid switching without losing concept accuracy through its sparse structure. Value: SHiRA ensures rapid adapter switching and minimal inference overhead, making it efficient and practical for deployment in resource-constrained environments such as mobile devices. SHiRA has shown superior performance in extensive experiments, outperforming traditional methods and achieving up to 2.7% higher accuracy in commonsense reasoning tasks. To evolve your company with AI, focus on identifying automation opportunities, defining KPIs, selecting suitable AI solutions, and implementing gradually. For AI KPI management advice, connect with us at [email protected]. Discover how AI can redefine your sales processes and customer engagement at itinai.com. List of Useful Links: AI Lab in Telegram @itinai – free consultation Twitter – @itinaicom #QualcommAI #SHiRA #LanguageModels #AIRevolution #AdaptationTechniques #productmanagement #ai #ainews #llm #ml #startup #innovation #uxproduct #artificialintelligence #machinelearning #technology #ux #datascience #deeplearning #tech #robotics #aimarketing #bigdata #computerscience #aibusiness #automation #aitransformation

QoQ and QServe: A New Frontier in Model Quantization Transforming Large Language Model Deployment https://lnkd.in/d6sqeQhJ Practical Solutions for Large Language Model Deployment We have practical solutions to make deploying large language models more efficient. Quantization simplifies data for quicker computations and better model performance. However, it's complex for large models due to their size and computational needs. Introducing the QoQ Algorithm Our QoQ algorithm, developed by researchers from MIT, NVIDIA, UMass Amherst, and MIT-IBM Watson AI Lab, refines quantization using progressive group quantization to maintain accuracy. This ensures computations are adapted to current-generation GPUs. Two-Stage Quantization Process The QoQ algorithm utilizes a two-stage quantization process, enabling operations on INT8 tensor cores and incorporating SmoothAttention to optimize performance further. QServe System for Efficient Deployment Our QServe system maximizes the efficiency of large language models, seamlessly integrating with GPU architectures and reducing quantization overhead by focusing on compute-aware weight reordering and fused attention mechanisms. Performance and Results Performance evaluations of the QoQ algorithm show substantial improvements, with throughput enhancements of up to 3.5 times compared to previous methods. QoQ and QServe significantly reduce the cost of deploying large language models. Evolve Your Company with AI Use QoQ and QServe to redefine your work processes. Identify automation opportunities, define KPIs, choose AI solutions that align with your needs, and implement gradually. Connect with us at [email protected] for AI KPI management advice and continuous insights into leveraging AI. Spotlight on a Practical AI Solution: AI Sales Bot Our AI Sales Bot from itinai.com/aisalesbot automates customer engagement 24/7 and manages interactions across all customer journey stages, redefining sales processes and customer engagement. List of Useful Links: AI Lab in Telegram @itinai – free consultation Twitter – @itinaicom #productmanagement #ai #ainews #llm #ml #startup #innovation #uxproduct #artificialintelligence #machinelearning #technology #ux #datascience #deeplearning #tech #robotics #aimarketing #bigdata #computerscience #aibusiness #automation #aitransformation

?? Embracing an Exponential Mindset in Product Management ?? The combination of people being extremely sceptical to being blown away from AI and its usage is quite fascinating. Those of you who have been around since ImageNet showed the potential of deep neuron networks year 2015 are likely less surprised of the improvements we have seen the last years compared to others, and are less likely to be strangers to the fact that since then, there is a strong correlation between compute and ai-performance. This correllation between compute and AI mean that one way then to guess what is comming tomorrow is to look at the progress of compute, and here I think is a clear sign for why it's hard for many to predict what is to come, the short version is that thinking linearily just won’t cut it anymore. Nvidia’s CEO, Jensen Huang, revealed that GPU processing power has 1000x in the last 10 years—and he predicts a 1000000x increase the next 10 years for ai models!?? The issue here is that if we look at what exists today and project into the future how this will change our society in 5-10 years, we can be wrong with a factor of 1000x or more. We HAVE to take this exponential growth into account. As product managers, learn from others such as Klarna, on how we can adapt in the close future is at least a bare minimum: 1?? Anticipate AI’s Future: AI is growing exponentially. Don’t just plan for what it can do today—build with the future in mind. Tools that assist now will soon automate entire workflows. For example, Klarna’s AI assistant already manages two-thirds of customer interactions in under 2 minutes ??. 2?? Prioritize Documentation & Data: AI thrives on structured knowledge. Klarna's internal AI answers 250k+ employee questions daily thanks to well-organized documentation. Ensure your internal resources are clear and accessible to boost AI performance ??. 3?? Adopt AI-First Design: AI isn’t just an add-on—it should be the core of your product. Think AI-first and continuously optimize user experiences. Klarna’s multilingual AI support shows how deeply integrated AI can improve customer service globally ??. ?? Embrace the exponential mindset now, and you’ll be ready to shape the future of product development for internal and external use. #AI #ProductManagement #Innovation #FutureProof ?? pcgamer https://lnkd.in/dEvU_naw ?? Klarna https://lnkd.in/d5QAAV5T