OpenLLaMA 7Bv2 is a cutting-edge language model, trained with a focus on delivering high-quality, contextually relevant text predictions. It leverages a diverse composite dataset that includes web-crawled data, scholarly articles, and a wide range of literature and question-answer pairs to ensure broad domain coverage and applicability.
Training Data
The model was trained on a composite dataset that includes:
Falcon refined-web dataset
starcoder datasets
Contributions from Wikipedia for encyclopedic knowledge
Academic papers from arXiv for scientific understanding
A vast collection of books spanning multiple genres
Stack Exchange data curated by RedPajama
Training Procedure
Learning Rate: Utilized a maximum learning rate of 3e-4 and a minimum learning rate of 3e-5.
Batch Size: Employed a batch size of 4 million tokens, optimizing the training process for both efficiency and performance.
Learning Rate Scheduler: The model's learning rate scheduling closely follows the strategy used in Llama2, ensuring gradual adjustments for optimal convergence.
