🚀 Introducing Fox-1: TensorOpera’s Pioneering Open-Source SLM!We are thrilled to introduce TensorOpera Fox-1, our cutting-edge 1.6B parameter small language model (SLM) designed to advance scalability and ownership in the generative AI landscape. Fox-1 stands out by delivering top-tier performance, surpassing comparable SLMs developed by industry giants such as Apple, Google, and Alibaba.What’s unique about Fox-1?🌟 Outstanding Performance (Small but Smart): Fox-1 was trained from scratch with a 3-stage data curriculum on 3 trillion tokens of text and code data in 8K sequence length. In various benchmarks, Fox-1 is on par or better than other SLMs in its class including Google’s Gemma-2B, Alibaba’s Qwen1.5-1.8B, and Apple’s OpenELM1.1B.🌟 Advanced Architectural Design: With a decoder-only transformer structure, 16 attention heads, and grouped query attention, Fox-1 is notably deeper and more capable than its peers (78% deeper than Gemma 2B, 33% deeper than Qwen1.5 - 1.8B, and 15% deeper than OpenELM 1.1B).🌟Inference Efficiency (Fast): On the TensorOpera serving platform with BF16 precision deployment, Fox-1 processes over 200 tokens per second, outpacing Gemma-2B and matching the speed of Qwen1.5-1.8B.🌟 Versatility Across Platforms: Fox-1's integration into TensorOpera’s platforms enables AI developers to build their models and applications on the cloud via TensorOpera AI Platform, and then deploy, monitor, and fine-tune them on smartphones and AI-enabled PCs via TensorOpera FedML platform. This offers cost efficiency, privacy, and personalized experiences within a unified platform.Why SLMs?1⃣ SLMs provide powerful capabilities with minimal computational and data needs. This “frugality” is particularly advantageous for enterprises and developers seeking to build and deploy their own models across diverse infrastructures without the need for extensive resources.2⃣ SLMs are also engineered to operate with significantly reduced latency and require far less computational power compared to LLMs. This allows them to process and analyze data more quickly, dramatically enhancing both the speed and cost-efficiency of inferencing, as well as responsiveness in generative AI applications.3⃣ SLMs are particularly well-suited for integration into composite AI architectures such as Mixture of Experts (MoE) and model federation systems. These configurations utilize multiple SLMs in tandem to construct a more powerful model that can tackle more complex tasks like multilingual processing and predictive analytics from several data sources.How to get started?We are releasing Fox-1 under the Apache 2.0 license. You can access the model from the TensorOpera AI Platform and Hugging Face.More details in our blogpost: https://t.co/nRemISpsXp…https://t.co/j1EsBS4edl

🎉 Introducing TensorOpera AI, Inc: A New Era in Our Journey! We are thrilled to announce a significant milestone in our journey. Two years ago, we embarked on an ambitious path with FedML, focusing primarily on federated learning. Today, as we look back on the tremendous growth and expansion of our product offerings, it’s clear that we’ve evolved into something much greater. To better represent the breadth and depth of our innovative solutions, we are excited to unveil our new identity: TensorOpera AI, Inc. 🤔 Why TensorOpera AI? Our new name, TensorOpera AI, is a testament to our commitment to blending cutting-edge technology with creativity. The term “Tensor” represents the foundational building blocks of artificial intelligence—emphasizing the critical role of data, computing power, and models in AI operations. “Opera,” on the other hand, brings to mind the rich and diverse world of the arts—encompassing poetry, music, dance, orchestration, and collaboration. This name reflects our vision for a generative AI future, characterized by multi-modality and complex, multi-model AI systems that are as harmonious and coordinated as a grand opera. 📈 Our Expanding Product Suite As TensorOpera AI, we are proud to offer two main product lines that cater to a wide range of needs within the AI community: TensorOpera AI Platform - Accessible at https://t.co/mKbyzriZyQ, this platform is a powerhouse for developers and enterprises aiming to build and scale their generative AI applications. Our platform excels in providing enterprise-grade features that include model deployment, AI agent APIs, serverless and decentralized GPU cloud operations for training and inference, and comprehensive tools for security and privacy. It’s designed to empower users to create, scale, and thrive in the AI ecosystem economically and efficiently. TensorOpera FedML - Available at https://t.co/HWftJA1QPO, this platform remains a leader in federated learning technology. It offers a zero-code, secure, and cross-platform solution that’s perfect for edge computing. The Edge AI SDK, part of TensorOpera FedML, ensures easy deployment across edge GPUs, smartphones, and IoT devices. Additionally, the platform’s MLOps capabilities simplify the decentralization and real-world application of machine learning, backed by years of pioneering research from our co-founders. 🚀 Looking Forward As TensorOpera AI, we remain dedicated to pushing the boundaries of what’s possible in generative AI. Our rebranding is not just a change of name, but a renewal of our promise to you—our community of developers, researchers, and innovators—to provide the tools and technology you need to succeed in this exciting era of AI. We invite you to join us at TensorOpera AI as we continue to orchestrate a smarter, more creative future together.

We are thrilled to announce our partnership with DENSO to empower fully on-premise training, development, and deployment of AI models via @FEDML_AI Nexus AI platform (https://t.co/7cKYybixvQ). As enterprises and organizations move fast toward bringing AI into their products and services, the need for privacy, security, full control, and ownership of the entire AI software stack becomes a critical requirement. This is especially true with the emergence of Generative AI models and applications, as data and AI models have become essential assets for any organization to obtain their competitive advantage. FEDML is committed to helping enterprises navigate the AI revolution with full ownership and control. By deploying FEDML Nexus AI platform on their own infrastructure (whether private cloud, on-premise servers, or hybrid), companies can provide their employees and customers with scalable, state-of-the-art GenAI capabilities, while giving them full control over their data, models, and computing resources. Our partnership with DENSO perfectly embodies our vision of delivering “Your” Generative AI Platform at Scale. Read more here: https://t.co/CMBgqOFrE1 via @VentureBeat

🔥 Start building your own fine-tuned Llama3 on FEDML Nexus AI! Open-sourced Llama3 70B is wildly good: it's on par with the performance of closed-source GPT-4 in Chatbot Arena Leaderboard (as of April 20th, 2024). This provides an excellent opportunity for enterprises and developers to own a high-performance self-hosted LLM customized on their private data. At FEDML, we are very excited to share our zero-code and serverless platform for fine-tuning Llama3-8B/70B, which requires no strong expertise and knowledge in AI and ML Infrastructure. We also have on-demand availability of H100 80GB GPUs at a very low price on FEDML cloud to be used directly when launching your fine-tuning jobs. You just need to: (1) Prepare your own training data (see instructions here: https://t.co/Kqme8Bln9P); (2) Set hyperparameters (or, use the default ones that the platform provides) (3) Click Launch! Read more details and get started here: https://t.co/FTAp1DikHS#Llama3 #serverlessfinetuning #fedml

🚀🚀Llama-3 + FEDML!Llama 3 is now available on FEDML Nexus AI to➤ Access and use APIs with $20 free credit and then atLlama3-8B - $0.1 / 1M TokenLlama3-70B - $0.9 / 1M Token➤ Deploy and serve on your dedicated servers with autoscale and advanced monitoring➤ Create powerful AI Agents with it using a fully integrated RAG pipeline➤ Fine-tune it with one click on FEDML Nexus AI StudioGet started here: https://t.co/cnEfbIfnpk