Category: Misc

Meta’s Llama collection of large language models are the most popular foundation models in the open-source community today, supporting a variety of use cases….

Meta’s Llama collection of large language models are the most popular foundation models in the open-source community today, supporting a variety of use cases. Millions of developers worldwide are building derivative models, and are integrating these into their applications. With Llama 3.1, Meta is launching a suite of large language models (LLMs) as well as a suite of trust and safety models…

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Synthetic data isn’t about creating new information. It’s about transforming existing information to create different variants. For over a decade, synthetic…

Synthetic data isn’t about creating new information. It’s about transforming existing information to create different variants. For over a decade, synthetic data has been used to improve model accuracy across the board—whether it is transforming images to improve object detection models, strengthening fraudulent credit card detection, or improving BERT models for QA. What’s new?

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The newly unveiled Llama 3.1 collection of 8B, 70B, and 405B large language models (LLMs) is narrowing the gap between proprietary and open-source models. Their…

The newly unveiled Llama 3.1 collection of 8B, 70B, and 405B large language models (LLMs) is narrowing the gap between proprietary and open-source models. Their open nature is attracting more developers and enterprises to integrate these models into their AI applications. These models excel at various tasks including content generation, coding, and deep reasoning, and can be used to power…

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Employing retrieval-augmented generation (RAG) is an effective strategy for ensuring large language model (LLM) responses are up-to-date and not…

Employing retrieval-augmented generation (RAG) is an effective strategy for ensuring large language model (LLM) responses are up-to-date and not hallucinated. While various retrieval strategies can improve the recall of documents for generation, there is no one-size-fits-all approach. The retrieval pipeline depends on your data, from hyperparameters like the chunk size…

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Businesses seeking to harness the power of AI need customized models tailored to their specific industry needs. NVIDIA AI Foundry is a service that enables enterprises to use data, accelerated computing and software tools to create and deploy custom models that can supercharge their generative AI initiatives. Just as TSMC manufactures chips designed by other
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Generative AI applications have little, or sometimes negative, value without accuracy — and accuracy is rooted in data. To help developers efficiently fetch the best proprietary data to generate knowledgeable responses for their AI applications, NVIDIA today announced four new NVIDIA NeMo Retriever NIM inference microservices. Combined with NVIDIA NIM inference microservices for the Llama
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NVIDIA today announced a new NVIDIA AI Foundry service and NVIDIA NIM™ inference microservices to supercharge generative AI for the world’s enterprises with the Llama 3.1 collection of openly available models, also introduced today.

Telecom wireless network design demands streamlined processes and standardized approaches. Network architects, engineers, and IT professionals are challenged…

Telecom wireless network design demands streamlined processes and standardized approaches. Network architects, engineers, and IT professionals are challenged with manually retrieving and customizing Topology and Orchestration Specification for Cloud Applications (TOSCA) templates to meet firm industry specifications. This leads to reduced productivity and increases the risk of human errors and…

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Telecom companies are challenged with consistently meeting service level agreements (SLAs) for end customers that ensure network quality of service. This…

Telecom companies are challenged with consistently meeting service level agreements (SLAs) for end customers that ensure network quality of service. This includes quickly troubleshooting network devices with complex issues, identifying root causes, and resolving issues efficiently at their network operations centers (NOCs). Current network troubleshooting and repair processes are often time…

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