OpenELM
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About
OpenELM is a state-of-the-art open language model developed by Qingqing Cao and his colleagues. It utilizes a layer-wise scaling strategy that allows for efficient allocation of model parameters, enhancing accuracy with a significantly reduced number of pre-training tokens. Specifically, OpenELM shows a 2.36% improvement in accuracy compared to similar models while requiring twice as few tokens for pre-training. The framework includes comprehensive resources for training and evaluation on publicly available datasets, ensuring transparent research practices. Alongside model weights and inference code, the release aims to empower the research community to advance open research efforts.
Platform
Features
• efficient nlp
• on-device and cloud compatibility
• layer-wise scaling for parameter optimization
• open training and inference framework
• publicly accessible datasets and training logs
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