# OpenLLaMA 7Bv2 Model Card

## Model Description

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.