rbelanec/train_record_42_1779207275
The rbelanec/train_record_42_1779207275 model is a 1 billion parameter instruction-tuned causal language model, fine-tuned by rbelanec from the Meta Llama-3.2-1B-Instruct architecture. This model has been specifically adapted using the 'record' dataset, achieving a validation loss of 0.3958. It is optimized for tasks related to the 'record' dataset, demonstrating specialized performance in that domain.
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Overview
The rbelanec/train_record_42_1779207275 model is a specialized 1 billion parameter language model, fine-tuned by rbelanec. It is based on the Meta Llama-3.2-1B-Instruct architecture, indicating its foundation in a robust instruction-following framework. The model underwent fine-tuning on a specific dataset referred to as the "record" dataset.
Key Training Details
During its training, the model achieved a validation loss of 0.3958 after processing 245,808,128 input tokens. The training procedure involved:
- Learning Rate: 2e-06
- Batch Size: 8 (for both training and evaluation)
- Optimizer: ADAMW_TORCH
- Epochs: 5
- LR Scheduler: Cosine with a 0.1 warmup ratio
Performance Metrics
The training results show a progressive decrease in training loss, with the validation loss reaching its lowest point at 0.3958 around the 2-epoch mark. Subsequent epochs saw fluctuations in validation loss, suggesting potential overfitting or further specialization to the training data.
Intended Use
Given its fine-tuning on the "record" dataset, this model is primarily intended for applications and tasks that align with the characteristics and content of that specific dataset. Users should consider its specialized nature when evaluating its suitability for broader or different use cases.