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IBM Releases Granite 4.0 3B Vision: A New Vision Language Model for Enterprise Grade Document Data Extraction

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IBM has announced the release of Granite 4.0 3B Vision, a vision-language model (VLM) engineered specifically for enterprise-grade document data extraction. Departing from the monolithic approach of larger multimodal models, the 4.0 Vision release is architected as a specialized adapter designed to bring high-fidelity visual reasoning to the Granite 4.0 Micro language backbone. This release […]

IBM Releases Granite 4.0 3B Vision: A New Vision Language Model for Enterprise Grade Document Data Extraction Read More »

Microsoft AI Releases Harrier-OSS-v1: A New Family of Multilingual Embedding Models Hitting SOTA on Multilingual MTEB v2

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Microsoft has announced the release of Harrier-OSS-v1, a family of three multilingual text embedding models designed to provide high-quality semantic representations across a wide range of languages. The release includes three distinct scales: a 270M parameter model, a 0.6B model, and a 27B model. The Harrier-OSS-v1 models achieved state-of-the-art (SOTA) results on the Multilingual MTEB

Microsoft AI Releases Harrier-OSS-v1: A New Family of Multilingual Embedding Models Hitting SOTA on Multilingual MTEB v2 Read More »

Google AI Introduces Gemini Embedding 2: A Multimodal Embedding Model that Lets Your Bring Text, Images, Video, Audio, and Docs into the Embedding Space

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Google expanded its Gemini model family with the release of Gemini Embedding 2. This second-generation model succeeds the text-only gemini-embedding-001 and is designed specifically to address the high-dimensional storage and cross-modal retrieval challenges faced by AI developers building production-grade Retrieval-Augmented Generation (RAG) systems. The Gemini Embedding 2 release marks a significant technical shift in how

Google AI Introduces Gemini Embedding 2: A Multimodal Embedding Model that Lets Your Bring Text, Images, Video, Audio, and Docs into the Embedding Space Read More »

Liquid AI’s New LFM2-24B-A2B Hybrid Architecture Blends Attention with Convolutions to Solve the Scaling Bottlenecks of Modern LLMs

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The generative AI race has long been a game of ‘bigger is better.’ But as the industry hits the limits of power consumption and memory bottlenecks, the conversation is shifting from raw parameter counts to architectural efficiency. Liquid AI team is leading this charge with the release of LFM2-24B-A2B, a 24-billion parameter model that redefines

Liquid AI’s New LFM2-24B-A2B Hybrid Architecture Blends Attention with Convolutions to Solve the Scaling Bottlenecks of Modern LLMs Read More »