OpenELM by Apple

Paid

Efficient open language model with layer-wise scaling

4.8

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Type

Saas

Company

Apple

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About OpenELM by Apple

OpenELM is a state-of-the-art open language model developed by Apple's Machine Learning Research team. It employs a layer-wise scaling strategy to efficiently allocate parameters within each transformer layer, achieving enhanced accuracy—for instance, a 2.36% improvement over OLMo while using 2× fewer pre-training tokens at the ~1B parameter scale. Unlike prior releases, OpenELM provides the complete training and evaluation framework on publicly available datasets, including training logs, multiple checkpoints, and pre-training configurations. Additionally, the release includes code to convert models to the MLX library for inference and fine-tuning on Apple devices. The model is accepted at the Efficient Systems for Foundation Models workshop at ICML 2024 and is available under open-source licensing (source code on GitHub).

Key Features

Layer-wise scaling strategy for efficient parameter allocation

2.36% accuracy improvement over OLMo with fewer pre-training tokens

Complete training and evaluation framework on public datasets

Includes training logs, multiple checkpoints, and pre-training configurations

Code to convert models to MLX for inference and fine-tuning on Apple devices

Open-source release with GitHub source code

Pros & Cons

Pros

Demonstrated 2.36% accuracy improvement over OLMo with 2× fewer pre-training tokens
Complete open-source release including training code and logs
Supports conversion to MLX for efficient inference on Apple hardware
Enhances reproducibility and transparency in LLM research

Best For

Open research in large language modelsTraining and evaluation of language models on public datasetsFine-tuning models on Apple devices using MLXInvestigating data and model biases

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FAQ

What is OpenELM?

OpenELM is a state-of-the-art open language model from Apple that uses a layer-wise scaling strategy to efficiently allocate parameters, achieving improved accuracy with fewer pre-training tokens.

How does OpenELM compare to OLMo?

At approximately 1 billion parameters, OpenELM shows a 2.36% accuracy improvement over OLMo while requiring 2 times fewer pre-training tokens.

What is included in the OpenELM release?

The release includes the complete training and evaluation framework, training logs, multiple checkpoints, pre-training configurations, and code to convert models to MLX for inference on Apple devices.

Is OpenELM open source?

Yes, the source code is available on GitHub and the release is designed to empower open research.

On which datasets was OpenELM trained?

OpenELM uses publicly available datasets for training and evaluation.