TensorOpera AI’s Post

For folks out there who are inundated with anything and everything generative AI, here's' a good summary by TechCrunch highlighting the open generative AI model - Meta Llama Llama is a family of models — not just one: ✅ Llama 8B ✅ Llama 70B ✅ Llama 405B The latest versions are Llama 3.1 8B, Llama 3.1 70B and Llama 3.1 405B, which was released in July 2024. They’re trained on web pages in a variety of languages, public code and files on the web, as well as synthetic data (i.e. data generated by other AI models). Llama 3.1 8B and Llama 3.1 70B are small, compact models meant to run on devices ranging from laptops to servers. Llama 3.1 405B, on the other hand, is a large-scale model requiring (absent some modifications) data center hardware. Llama 3.1 8B and Llama 3.1 70B are less capable than Llama 3.1 405B, but faster. They’re “distilled” versions of 405B, in point of fact, optimized for low storage overhead and latency. Like other generative AI models, Llama can perform a range of different assistive tasks, like coding and answering basic math questions, as well as summarizing documents in eight languages (English, German, French, Italian, Portuguese, Hindi, Spanish and Thai). Most text-based workloads — think analyzing files like PDFs and spreadsheets — are within its purview; https://lnkd.in/eQ-kjB-y

Announcing the general availability of OCI Generative AI   The following LLM's are available for text generation use cases:   a. Cohere Command in 52Bn and 6n parameter sizes b. Meta Llama 2 70-billion parameter model   (4096 token length maximum)   Cohere Embed V3.0 for embeddings generations   a. Embed English and English Light V3 b. Embed Multilingual and Multilingual Light V3   Available both as On-demand or Dedicated AI Clusters

AI, production grade: Need performance? If you haven't heard of Together.ai, they provide the best performance that I know of and have high speed (low latency) infiniband interconnect for distributed training. Turns out they're launching Meta's Llama3.2 models. https://lnkd.in/gjc-uTai

Mistral AI Introduces Les Ministraux: Ministral 3B and Ministral 8B- Revolutionizing On-Device AI High-Performance AI Models for On-Device Use To address the challenges of current large-scale AI models, we need high-performance AI models that can operate on personal devices and at the edge. Traditional models rely heavily on cloud resources, which can lead to privacy concerns, increased latency, and higher costs. Moreover, cloud dependency is not ideal for offline usage. Introducing Ministral 3B and Ministral 8B Mistral AI has launched two innovative models, Ministral 3B and Ministral 8B, designed to enhance AI capabilities on devices without needing cloud support. These models, known as les Ministraux, enable powerful language processing directly on devices. This shift is crucial for sectors like healthcare, industrial automation, and consumer electronics, allowing applications to perform advanced tasks locally, securely, and cost-effectively. They represent a significant advancement in how AI can interact with the physical world, providing greater autonomy and flexibility. Technical Details and Benefits Les Ministraux are engineered for efficiency and performance. With 3 billion and 8 billion parameters, these transformer-based models are optimized for lower power use while maintaining high accuracy. They utilize pruning and quantization techniques to minimize computational demands, making them suitable for devices with limited hardware, such as smartphones and embedded systems. Ministral 3B is tailored for ultra-efficient deployment, while Ministral 8B is designed for more complex language tasks. Importance and Performance Results The impact of Ministral 3B and 8B goes beyond technical specs. They tackle critical issues in edge AI, such as reducing latency and enhancing data privacy by processing information locally. This is vital for fields like healthcare and finance. Early tests show that Ministral 8B significantly improves task completion rates compared to existing on-device models while remaining efficient. These models also allow developers to create applications that function without constant internet access, ensuring reliability in remote or low-connectivity areas, which is essential for field operations and emergency responses. Conclusion The launch of les Ministraux, Ministral 3B and 8B, is a pivotal moment in the AI industry, bringing powerful computing directly to edge devices. Mistral AI’s commitment to optimizing these models for on-device use solves key challenges related to privacy, latency, and cost, making AI more accessible across various fields. By offering top-tier performance without relying on the cloud, these models enable seamless, secure, and efficient AI operations at the edge. This not only improves user experiences but also opens new possibilities for... https://lnkd.in/dUd_iDud https://lnkd.in/dCrCev-B

Revolutionizing Fine-Tuned Small Language Model Deployments: Introducing Predibase’s Next-Gen Inference Engine Predibase announces the Predibase Inference Engine, their new infrastructure offering designed to be the best platform for serving fine-tuned small language models (SLMs). The Predibase Inference Engine dramatically improves SLM deployments by making them faster, easily scalable, and more cost-effective for enterprises grappling with the complexities of productionizing AI. Built on Predibase’s innovations–Turbo LoRA and LoRA eXchange (LoRAX)–the Predibase Inference Engine is designed from the ground up to offer a best-in-class experience for serving fine-tuned SLMs. Technical Breakthroughs in the Predibase Inference Engine At the heart of the Predibase Inference Engine are a set of innovative features that collectively enhance the deployment of SLMs: ✅ LoRAX: LoRA eXchange (LoRAX) allows for the serving of hundreds of fine-tuned SLMs from a single GPU. This capability significantly reduces infrastructure costs by minimizing the number of GPUs needed for deployment. It’s particularly beneficial for businesses that need to deploy various specialized models without the overhead of dedicating a GPU to each model. ✅ Turbo LoRA: Turbo LoRA is our parameter-efficient fine-tuning method that accelerates throughput by 2-3 times while rivaling or exceeding GPT-4 in terms of response quality. These throughput improvements greatly reduce inference costs and latency, even for high-volume use cases. ✅ FP8 Quantization: Implementing FP8 quantization can reduce the memory footprint of deploying a fine-tuned SLM by 50%, leading to nearly 2x further improvements in throughput. This optimization not only improves performance but also enhances the cost-efficiency of deployments, allowing for up to 2x more simultaneous requests on the same number of GPUs. ✅ GPU Autoscaling: Predibase SaaS deployments can dynamically adjust GPU resources based on real-time demand. This flexibility ensures that resources are efficiently utilized, reducing waste and cost during periods of fluctuating demand. Read our full article here: https://lnkd.in/gQX2Qk9h Predibase #ai

As Generative AI makes the transition from hype to tangible business value creation, partnerships like the one between Mistral AI and Snowflake will meaningfully accelerate the AI era of business over the next 90 days. As anyone who knows me knows…I’m +1000 here for that (maybe, +10000 actually ;) 👀👀👀🏆🏆🏆. A few excerpts from the VentureBeat coverage along with a link to the article below: “Mistral, the Paris-based AI startup that raised Europe’s largest-ever seed round in June 2023 and has since become a rising star in the global AI domain.” “Under the partnership, Snowflake said it plans to bring all open large language models (LLMs) built by Mistral into its data cloud, making them directly available to customers looking to build LLM apps.” “'By partnering with Mistral AI, Snowflake is putting one of the most powerful LLMs on the market directly in the hands of our customers, empowering every user to build cutting-edge, AI-powered apps with simplicity and scale,' Sridhar Ramaswamy, who recently took over as the CEO of Snowflake, said in a press statement.” “Now, with the partnership and investment in Mistral AI, the company is adding another open and highly performant family of models into Cortex for LLM app development. This not only includes the all-new Mistral Large, which sits just behind GPT-4 and outperforms Claude 2, Gemini Pro and GPT 3.5 with native proficiency across five languages and a context window of 32K tokens, but also Mixtral 8x7B and Mistral 7B models”

Together AI Optimizing High-Throughput Long-Context Inference with Speculative Decoding: Enhancing Model Performance through MagicDec and Adaptive Sequoia Trees Speculative decoding is emerging as a vital strategy to enhance high-throughput long-context inference, especially as the need for inference with large language models (LLMs) continues to grow across numerous applications. Together AI’s research on speculative decoding tackles the problem of improving inference throughput for LLMs that deal with long input sequences and large batch sizes. This research provides crucial insights into overcoming memory bottlenecks during inference, particularly when managing long-context scenarios. Context and Challenges in Long-Context Inference As the use of LLMs increases, the models are tasked with handling more extensive context lengths. Applications like information extraction from large document sets, synthetic data generation for fine-tuning, extended user-assistant conversations, and agent workflows all require the models to process sequences that span thousands of tokens. This demand for high-throughput processing at long context lengths presents a technical challenge, largely due to the extensive memory requirements for storing key-value (KV) caches. These caches are essential for ensuring the model can efficiently recall earlier parts of long input sequences. Traditionally, speculative decoding, which leverages unused computational resources during memory-bound decoding phases, has yet to be considered suitable for high-throughput situations. The prevailing assumption was that decoding would be compute-bound for large batch sizes, and GPU resources would already be fully utilized, leaving no room for speculative techniques. However, Together AI’s research counters this assumption. They demonstrate that decoding becomes memory-bound again in scenarios with large batch sizes and long sequences, making speculative decoding a viable and advantageous approach. Key Innovations: MagicDec and Adaptive Sequoia Trees Together AI introduces two critical algorithmic advancements in speculative decoding: MagicDec and Adaptive Sequoia Trees, designed to enhance throughput under long-context and large-batch conditions. 1. MagicDec: The primary bottleneck during long-context, large-batch decoding is loading the KV cache. MagicDec addresses this by employing a fixed context window in the draft model, enabling the draft model to function more quickly than the target model. By fixing the context window size, the draft model’s KV cache is significantly smaller than that of the target model, which speeds up the speculative process. Interestingly, the approach also allows using a very large and powerful draft model. Using the full target model as the draft becomes feasible under this regime because the bottleneck no longer loads the model parameters. MagicDec leverages several strategies from other models, like TriForce and StreamingLLM. It uses a...

Generative AI applications are still applications, so you need the following: ·     Operational databases to support the user experience for interaction steps outside of invoking generative AI models. ·     Data lakes to store your domain-specific data, and analytics to explore them and understand how to use them in generative AI. ·     Data integrations and pipelines to manage (sourcing, transforming, enriching, and validating, among others) and render data usable with generative AI. ·     Governance to manage aspects such as data quality, privacy and compliance to applicable privacy laws, and security and access controls. In this blog post, you will find a framework to implement generative AI applications enriched and differentiated with your data. https://lnkd.in/g5HReKAY

🚀 Excited to share my latest project: Completely Free Data Analysis with Generative AI! 🚀 I've combined the power of Groq API, PandasAI (YC W24), and Streamlit to create a seamless data analysis experience. The Generative AI model driving this is none other than Llama3. Here's why this project stands out: 💡 Llama3 Model: Harnessing the advanced capabilities of Llama3 for robust data analysis and insights. ⚡⚡ Groq's Speed: Utilizing Groq's LPU ( Language Processing Unit) to achieve unparalleled speed and efficiency in AI computations, setting a new standard in the AI race. Groq is the fastest LLM in the AI era, no doubt about that. what is Groq?  Groq is the AI infrastructure company that builds the world’s fastest AI inference technology.  The LPU™ Inference Engine by Groq is a hardware and software platform that delivers exceptional compute speed, quality, and energy efficiency.  Groq, headquartered in Silicon Valley, provides cloud and on-prem solutions at scale for AI applications. The LPU and related systems are designed, fabricated, and assembled in North America.  what is LPU inference Engine? An LPU Inference Engine, with LPU standing for Language Processing Unit™, is a hardware and software platform that delivers exceptional compute speed, quality, and energy efficiency. This new type of end-to-end processing unit system provides the fastest inference for computationally intensive applications with sequential components, such as AI language applications like Large Language Models (LLMs). why is it to much faster than GPUs for LLMs and GenAI? The LPU is designed to overcome the two LLM bottlenecks: compute density and memory bandwidth. An LPU has greater compute capacity than a GPU and CPU in regards to LLMs. This reduces the amount of time per word calculated, allowing sequences of text to be generated much faster. Additionally, eliminating external memory bottlenecks enables the LPU Inference Engine to deliver orders of magnitude better performance on LLMs compared to GPUs. 📊 PandasAI (YC W24) Integration: Leveraging PandasAI for enhanced data manipulation and analysis, making the process intuitive and user-friendly. 🌐 Streamlit for Visualization: Employing Streamlit to create interactive and visually appealing data dashboards, ensuring that the insights are not just accurate but also accessible. 🗣️ Talk with Your Data: Interact with your data using simple English language commands, making data analysis as easy as having a conversation. The best part? It's completely free and accessible for anyone interested in diving into data analysis with the latest AI technologies. In this demo, I analyze Denmark Covid-19 data with just simple natural language commands, showcasing how easy and powerful this tool is. A huge thanks to Jonathan Ross,the CEO of Groq, for making this amazing fastest AI in the GenAI era possible! Also, huge thanks to Gabriele Venturi, the CEO of PandasAI (YC W24), who makes data analysis super easy!

The potential of Generative AI and Large Language Models (LLMs) for enterprises is massive.🌐💡 Snowflake’s single platform has already helped customers break down data silos and enabled them to bring more types of development directly to their data. This includes the ability to run and fine-tune leading LLMs in Snowflake using Snowpark Container Services, get smarter about your data with built-in LLMs, and boost productivity with LLM-powered experiences. Read More: https://lnkd.in/ei7Dizrq #Cogwise #CogwiseAI #AITools #AI #Snowflake #GenerativeAI #LLMs #TechInnovation #DataScience #FutureOfTech #Productivity #DataIntegration #AIInEnterprises