layai/syn-youtube-dict
The layai/syn-youtube-dict model is an 8 billion parameter language model fine-tuned from Meta-Llama-3-8B. It was trained on an unspecified dataset, achieving a validation loss of 3.6060 and an accuracy of 0.4585 on its evaluation set. This model is a general-purpose fine-tune of a Llama 3 base, with its specific differentiators and intended applications requiring further information.
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Model Overview
The layai/syn-youtube-dict model is an 8 billion parameter language model, fine-tuned from the robust Meta-Llama-3-8B architecture. While the specific dataset used for fine-tuning is not detailed, the training process involved standard hyperparameters including a learning rate of 5e-05, a train_batch_size of 40, and 3 epochs of training.
Performance Metrics
During its training, the model achieved a validation loss of 3.6060 and an accuracy of 0.4585 on the evaluation set after 500 steps. Subsequent evaluation steps showed a slight increase in validation loss, reaching 4.0823 by 2000 steps, with accuracy remaining around 0.45-0.46.
Training Configuration
Key training hyperparameters included:
- Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- LR Scheduler: Cosine
- Gradient Accumulation: 4 steps
- Total Train Batch Size: 160
Limitations
As the model card indicates, more information is needed regarding its specific intended uses, limitations, and the nature of the training and evaluation data. Developers should exercise caution and conduct further testing to determine its suitability for particular applications, given the lack of detailed information on its fine-tuning objective.