Apple recently unveiled the OpenELM family, a set of efficient language models designed to provide accurate results on devices like laptops using fewer training tokens than other AI models. OpenELM utilizes a layered scaling strategy to allocate parameters within each layer of the model, allowing users to achieve more precise results for specific tasks. 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.
In testing, researchers found that OpenELM performs more efficiently than similar LLMs like Elm tree, achieving 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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