Focus on Openness: It emphasizes reproducibility and transparency in large language models. This is achieved by releasing the complete training framework, evaluation methods, and code for inference and fine-tuning. State-of-the-Art Performance: OpenELM achieves high accuracy compared to other models with similar parameter size. The paper provides an example where a one billion parameter OpenELM model outperforms another model on a benchmark. Efficient Allocation: OpenELM uses a special strategy to distribute parameters within the model, leading to better performance without needing as much training data.
