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)

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

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

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

FastGen: Cutting GPU Memory Costs Without Compromising on LLM Quality https://lnkd.in/dXX2YrjW Practical AI Solutions for Efficient LLM Inference FastGen: Cutting GPU Memory Costs Without Compromising on LLM Quality FastGen is a technique designed to enhance the efficiency of large language models (LLMs) without compromising on quality. It reduces GPU memory usage while maintaining high-quality language model performance. This is achieved through lightweight model profiling and adaptive key-value caching. FastGen achieves this by evicting long-range contexts on attention heads through an adaptive KV cache, effectively reducing GPU memory usage with minimal impact on generation quality. The introduced adaptive KV Cache compression aims to reduce the memory footprint of generative inference for LLMs. For companies seeking to leverage AI, FastGen offers a practical solution to reduce GPU memory costs without sacrificing LLM quality. It provides a competitive edge in the AI landscape by enhancing model efficiency and inference speed. AI Implementation Guidelines 1. Identify Automation Opportunities: Find customer interaction points that can benefit from AI. 2. Define KPIs: Ensure AI initiatives have measurable impacts on business outcomes. 3. Select an AI Solution: Choose tools that align with your needs and offer customization. 4. Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously. For AI KPI management advice, contact us at [email protected]. For continuous insights into leveraging AI, stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom. AI Sales Bot from itinai.com Consider the AI Sales Bot from itinai.com/aisalesbot, designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com. 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

A good read on AI strategies. Also learned in the article there's an robot pet named "Loona" on the market for $499. Does it need walks? "To guide this transition, leading tech companies and universities offer actionable strategies for human-centered AI. In this post, I’ll share UX frameworks from IBM, Google, Microsoft, and Carnegie Mellon University— providing insights and resources for navigating the rapid evolution of AI technologies and tools."

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

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

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

Great piece by Rob Chappell on 5 AI frameworks for designers as they look to bring AI into products in meaningful and useful ways. Developed by Google, Microsoft, IBM iX, and Carnegie Mellon University's HCI group. https://lnkd.in/gcKiinXd #ai #productdesign #aidesign #aiux