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Large language models (LLMs) like GPT-4 and Llama-2 are powerful but require significant computational resources, making them impractical for smaller devices. Attention-based transformer models, in particular, have high memory demands and quadratic computational complexity, which limits their efficiency. State Space Models (SSMs), such as Mamba, offer an alternative with lower complexity, but their limited memory… Read more

Ai2 released Tülu 3, a model that makes fine-tuning open-source LLMs easier and get its performance closer to closed LLMs like GPT-4o.Read More Read more

LLaVA-o1 breaks down the answer into multiple reasoning components and uses inference-time scaling to optimize each stage.Read More Read more

Amazon strengthens its position in the AI race with an additional $4 billion investment in Anthropic, committing to custom silicon and cloud dominance to compete with Microsoft and Google.Read More Read more

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As their self-correction and self-reasoning improve, the latest LLMs find new capabilities at a rate that makes it harder to measure everything they can do. Read more