Key Points
1. Introduction: Language models (LMs) have become commercially valuable but are often gated behind proprietary interfaces, making details of their training data and architectures undisclosed.
2. Open LM Release: OLMo is introduced as a state-of-the-art, truly open language model and framework, encompassing model weights, training data, and evaluation code, with the goal of empowering the research community and inspiring innovation.
3. Model Framework: OLMo framework includes multiple training checkpoints, training logs, ablations, training metrics, and inference code, providing comprehensive resources for building and researching language models.
4. Model Architecture: OLMo adopts a decoder-only transformer architecture and includes improvements over the vanilla transformer, such as non-parametric layer norm, SwiGLU activation function, RoPE positional embeddings, and a modified vocabulary size.
5. Pretraining Data: The Dolma pretraining dataset is described as a diverse, multi-source corpus and is released for open research, accompanied by a pipeline for dataset curation and analysis tools.
6. Evaluation Framework: OLMo provides tools for downstream and perplexity-based evaluation, enabling comparisons with other models and demonstrating fit to distributions of language on various data sources.
7. Pretraining Setup: Details of the distributed training framework, optimizer settings, data preparation, and hardware used for pretraining OLMo models are provided.
8. Environmental Impact: The total energy consumption and carbon emissions during pretraining OLMo models are estimated and compared with other models, highlighting efforts to reduce future emissions and promote open research.
9. Open Research Support: OLMo artifacts and resources, including model weights, training data, evaluation code, and adaptation tools, are released under a permissive license to encourage open and collaborative research efforts.
Summary
The research paper discussed the commercial significance of language models in natural language processing. It emphasized the impact of large-scale pretraining and human annotation for alignment on their commercial value, as well as the issue of proprietary interfaces and undisclosed details surrounding the largest models. The paper introduced OLMo, a state-of-the-art open language model and framework, releasing not only model weights but also the entire framework, including training data and code for training and evaluation. It highlighted the importance of providing full access to open language models for the research community to enable scientific study and understanding of model biases and risks.
The paper also compared OLMo with other open language model releases, emphasizing the need for open access to pretraining datasets for better understanding of language model capabilities and limitations. Additionally, it provided details on the OLMo framework, including the OLMo models, pre-training dataset (Dolma), and evaluation framework. The paper outlined the model architecture, hyperparameters, optimizer settings, and hardware used for training the language models.
Furthermore, it discussed the carbon emissions and power consumption estimates during model pretraining, highlighting the potential environmental impacts. The paper concluded by expressing the intention to continuously support and extend OLMo, bringing in different model sizes, modalities, datasets, safety measures, and evaluations to empower the open research community. It also acknowledged the contributions of numerous individuals and institutions to the OLMo project.
Reference: https://arxiv.org/abs/2402.00838