layai/syn-dataaug-news-vanilla
layai/syn-dataaug-news-vanilla is an 8 billion parameter language model fine-tuned from Meta-Llama-3-8B. This model was trained on an unspecified dataset, achieving an accuracy of 0.8162 on its evaluation set. It is intended for general language generation tasks, with its fine-tuning potentially optimizing it for specific data augmentation or news-related applications, though further details are not provided.
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Model Overview
layai/syn-dataaug-news-vanilla is an 8 billion parameter language model derived from the Meta-Llama-3-8B architecture. It has undergone fine-tuning on an undisclosed dataset, demonstrating a loss of 1.3088 and an accuracy of 0.8162 on its evaluation set.
Training Details
The model was trained using the following key hyperparameters:
- Learning Rate: 5e-05
- Batch Size: 40 (train and eval)
- Gradient Accumulation Steps: 4, resulting in a total effective batch size of 160
- Optimizer: Adam with default betas and epsilon
- LR Scheduler: Cosine type
- Epochs: 3.0
Potential Use Cases
Given its base architecture and the 'syn-dataaug-news-vanilla' naming convention, this model is likely suitable for:
- General text generation: Leveraging the capabilities of the Llama-3-8B base.
- Data augmentation: Potentially generating synthetic data for specific domains, possibly news-related.
- News content processing: Tasks such as summarization, classification, or generation within the news domain, although specific optimizations are not detailed.
Further information regarding its specific intended uses, limitations, and the nature of its training data is not provided in the available documentation.