OpenELM` is an open-source large language model developed by Apple, offering unprecedented transparency and accessibility in the field of natural language processing. It utilizes a decoder-only transformer architecture with several key techniques such as bias removal, normalization, positional encoding, attention mechanisms, feed-forward networks, and layer-wise scaling to optimize parameter allocation within the transformer architecture. OpenELM has demonstrated impressive performance across various benchmarks, outshining many of its open-source counterparts while requiring significantly less training data. The model can be fine-tuned using MonsterAPI on custom datasets, allowing for efficient retraining without extensive modifications. Fine-tuning OpenELM results in faster models that can perform similarly to commercial LLMs at a lower inference cost.