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Large Language Model

Fireworks AI has unveiled Firefunction-v2: A freely accessible weights function calling model featuring function calling capacity that matches GPT4o. Interestingly, it operates at a speed that’s 2.5 times faster and costs just a tenth of the price.

Fireworks AI recently launched Firefunction-v2, an open-source function-calling model aiming to deliver superior performance in real-world applications. The model integrates with multi-turn conversations, instruction following, and parallel function calling, providing a powerful and effective solution comparable to more advanced models such as GPT-4o, but with increased speed, better functionality, and lower costs. Firefunction-v2's robustness and…

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Essential Measures for Assessing Big Language Models (LLMs)

Evaluating Large Language Models (LLMs) is a difficult task, as real-world problems are quite complex and ever-changing. Conventional benchmarks often fail to provide a holistic picture of LLMs' performance. Here are some key metrics recently highlighted in a LinkedIn post: 1. MixEval: Designed to ensure balance between user queries and effective grading, MixEval combines real-world user…

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Groundbreaking AI Innovations by Meta FAIR: Improving Creativity, Productivity, and Accountability in Open Science AI Investigation and Progress

Meta's Fundamental AI Research (FAIR) team has announced several significant advances in the field of artificial intelligence, reinforcing their commitment to collaboration, openness, and responsible artificial intelligence development. With a focus on principles of excellence and scalability, the team's aim is to foster cutting-edge innovation. Meta FAIR has launched six key research artifacts which include innovative…

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Meta FAIR’s Cutting-Edge AI Launches: Augmenting Creativity, Productivity, and Accountability in Transparent AI Science Explorations and Advancement.

Meta's Fundamental AI Research (FAIR) team has made significant advancements and contributions to AI research, models, and datasets recently that align with principles of openness, collaboration, quality, and scalability. Through these, the team aims to encourage innovation and responsible development in AI. Meta FAIR has made six key research artifacts public, as part of an aim…

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Together AI presents the Mixture of Agents (MoA): a pioneering AI structure that utilizes the combined powers of numerous LLMs to enhance top-notch quality.

Together AI has announced an advancement in artificial intelligence with a new approach called the Mixture of Agents (MoA), also referred to as Together MoA. This model employs the combined strengths of multiple large language models (LLMs) to deliver increased performance and quality, setting a new standard for AI. The MoA's design incorporates layers, each containing…

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Together AI presents the Mixture of Agents (MoA): a novel AI infrastructure that enhances top-tier quality by collaboratively harnessing the capabilities of various Large Language Models (LLMs).

AI organization Together AI has made a significant step in AI by introducing a Mixture of Agents (MoA) approach, Together MoA, which integrates the strengths of multiple large language models (LLMs) to boost quality and performance, setting new AI benchmarks. MoA uses a layered design, with each level having several LLM agents. These agents use the…

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Introducing DeepSeek-Coder-V2 from DeepSeek AI, a pioneering open-source AI model that outperforms GPT4-Turbo in coding and mathematics tasks. Remarkably, it supports up to 338 languages and a context length of 128K.

Code intelligence, which uses natural language processing and software engineering to understand and generate programming code, is an emerging area in the technology sector. While tools like StarCoder, CodeLlama, and DeepSeek-Coder are open-source examples of this technology, they often struggle to match the performance of closed-source tools such as GPT4-Turbo, Claude 3 Opus, and Gemini…

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Microsoft Research Introduces AutoGen Studio: A Groundbreaking Low-Code Platform Transforming Multi-Agent AI Workflow Creation and Implementation

Microsoft Research has recently unveiled AutoGen Studio, a groundbreaking low-code interface meant to revolutionize the creation, testing, and implementation of multi-agent AI workflows. This tool, an offshoot of the successful AutoGen framework, aspires to democratize complex AI solution development by minimizing coding expertise requirements and fostering an intuitive, user-friendly environment. AutoGen, initially introduced in September…

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