Model Overview

rbelanec/train_record_42_1773765559 is a 1 billion parameter language model, fine-tuned from the meta-llama/Llama-3.2-1B-Instruct base model. It was trained on the 'record' dataset over 5 epochs, demonstrating a final validation loss of 0.8647. The training involved processing over 245 million input tokens, utilizing a cosine learning rate scheduler and the AdamW optimizer.

Key Characteristics

• Base Model: Fine-tuned from meta-llama/Llama-3.2-1B-Instruct.
• Parameter Count: 1 billion parameters, offering a relatively compact model size.
• Training Data: Specifically fine-tuned on the 'record' dataset.
• Performance: Achieved a validation loss of 0.8647 on the evaluation set.
• Training Configuration: Utilized a learning rate of 5e-05, a batch size of 8, and 5 training epochs.

Intended Use Cases

This model is primarily suited for applications and research focused on tasks related to the 'record' dataset due to its specialized fine-tuning. Its smaller parameter count makes it potentially suitable for environments with limited computational resources where performance on the 'record' dataset is critical.