ai
In the rapidly evolving landscape of artificial intelligence, Voyage AI has unveiled its latest embedding models, Voyage 3 and Voyage 3-lite. These models redefine retrieval quality, latency, and cost efficiency, setting a new standard in the realm of AI-driven data processing. Overview of Models Voyage 3 and its lighter counterpart,
voyage-ai
In the rapidly evolving world of artificial intelligence, efficiency and performance are key. Voyage AI has recently announced its latest breakthrough in embedding models: Voyage-3 and Voyage-3-lite. These models are designed to revolutionize the field with their superior retrieval quality, cost efficiency, and latency improvements. Voyage-3 stands out by outperforming
azure-ai
The Azure AI Phi-3.5-MoE-Instruct model, a groundbreaking addition to Microsoft's Phi-3.5 series, is revolutionizing the landscape of artificial intelligence with its innovative architecture and impressive capabilities. Model Architecture At the heart of the Phi-3.5-MoE-Instruct is the Mixture-of-Experts (MoE) model architecture, which includes 16 experts, each
together-ai
Together AI continues to push the boundaries of artificial intelligence with their latest development: an embedding model capable of processing up to 150 million tokens. This new offering is a part of Together AI's broader suite of solutions aimed at enhancing the capabilities of AI for various business
together-ai
Together AI has introduced a range of powerful AI models boasting between 21.1 billion and 41 billion parameters. These models stand out due to their impressive capabilities and competitive pricing of just $0.80 per 1 million tokens for both input and output. Here’s a closer look at
cerebras
The tech industry is abuzz with the latest release from Cerebras: the Llama 3.1-70B. This large language model (LLM) offers groundbreaking performance, cost efficiency, and high precision, making it a game-changer for developers and enterprises alike. Unmatched Performance Cerebras Inference delivers an astonishing 450 tokens per second for Llama