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README.md

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+# 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.