Apple has recently unveiled the OpenELM family, a collection of language models designed to deliver precise results on devices such as laptops using fewer training tokens than other AI models. 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.
To achieve this goal, OpenELM employs a layered scaling strategy that efficiently allocates parameters within each layer of the model, focusing on specific tasks. This approach enables users to achieve more precise results while using fewer resources.
Researchers have tested the models on various devices such as 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.
It is 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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