Apple has unveiled the OpenELM family, a collection of language models that are designed to deliver accurate results on devices such as laptops while using fewer training tokens than other AI models. The OpenELM models use a layered scaling strategy to efficiently allocate parameters within each layer, enabling users to achieve more precise outcomes. The family consists of four large language models with varying sizes: 270 million, 450 million, 1.1 billion, and 3 billion parameters. Each model has two versions: pre-trained and optimized for specific purposes.
The researchers behind OpenELM have tested the models on devices like a MacBook Pro with M2 Max SoC and a computer with Intel i9-13900KF CPU and NVIDIA RTX 4090 GPU. The results showed that OpenELM performs more efficiently than similar LLMs like Elm tree, offering a 2.36 percent accuracy improvement while requiring fewer pre-training tokens.
However, it’s important to note that OpenELM has been trained with publicly available datasets without any security guarantees, which could result in inaccurate or compromised results.
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