Apple releases OpenELM at 270M to 3B, with parameters spread unevenly across layers

Apple posted OpenELM to arXiv on April 22, 2024, a family of four language models at 270M, 450M, 1.1B and 3B parameters, each also released in an instruction-tuned version. The name stands for Open-source Efficient Language Models, and the efficiency comes from layer-wise scaling, which spreads the parameter budget unevenly across the transformer’s layers instead of giving every layer the same shape. Table 1 of the paper gives the 1.1B model an average of 45.93% across the OpenLLM leaderboard tasks against 43.57% for OLMo 1.2B, a gap the authors state as 2.36%, reached on 1.5T pre-training tokens against OLMo’s 3.0T.

Layer-wise scaling varies the number of attention heads and the width of the feed-forward network from layer to layer, keeping layers near the input narrower and widening them towards the output, so a layer late in the stack holds more parameters than an early one. Apple trained the four variants for 350k iterations with its CoreNet library on public data only, drawing on RefinedWeb, a deduplicated PILE, a subset of RedPajama and a subset of Dolma v1.6, about 1.8T tokens in total. Text is filtered and tokenised on the fly rather than pre-tokenised, using the same tokeniser as Llama.

The paper benchmarks the models on an Apple MacBook Pro with an M2 Max chip and 64 GiB of RAM running macOS 14.4.1, through a port of OpenELM to MLX v0.10.0, and separately on a Linux workstation with an Intel i9-13900KF and an NVIDIA RTX 4090. On the MacBook in BFloat16, Apple measures the 270M model generating 212.40 tok/s and the 3B model 33.96 tok/s, with the 4-bit versions at 256.35 and 60.33 tok/s. On the CUDA machine OpenELM 1.08B is slower than OLMo 1.18B, at 92.15 tok/s against 203.40, which the authors attribute to a naive RMSNorm implementation and to OpenELM having 113 normalisation layers where OLMo has 33.

Apple presents the release as the complete framework rather than weights and inference code, covering the training and evaluation code for public datasets, the training logs, multiple checkpoints, the pre-training configurations and the code that converts a model to MLX for inference and fine-tuning on Apple devices, with pre-converted bf16 and 4-bit checkpoints for all four sizes. The eight models are on Hugging Face and the code is in CoreNet. Both the model cards and CoreNet name the terms as the Apple sample code license, which is Apple’s own license and not one of the standard open source licenses.