rbelanec/train_record_42_1779354540
The rbelanec/train_record_42_1779354540 model is a 1 billion parameter language model fine-tuned by rbelanec based on the Meta Llama-3.2-1B-Instruct architecture. This model was fine-tuned on the 'record' dataset, achieving a validation loss of 0.3537 over one epoch. It is intended for tasks related to the 'record' dataset, offering a specialized instruction-tuned variant of the Llama 3.2 series.
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Model Overview
rbelanec/train_record_42_1779354540 is a 1 billion parameter language model, fine-tuned from the meta-llama/Llama-3.2-1B-Instruct base model. The fine-tuning process was conducted using the 'record' dataset, with a focus on adapting the model's capabilities to this specific data distribution.
Training Details
The model underwent a single training epoch with a learning rate of 2e-06 and a batch size of 8 for both training and evaluation. The optimizer used was AdamW_Torch with standard beta values and an epsilon of 1e-08. A cosine learning rate scheduler with a warmup ratio of 0.1 was employed. During training, the model processed approximately 49 million input tokens, achieving a final validation loss of 0.3537.
Key Characteristics
- Base Model: Meta Llama-3.2-1B-Instruct
- Parameter Count: 1 billion
- Fine-tuning Dataset: 'record' dataset
- Achieved Performance: Validation loss of 0.3537
Potential Use Cases
This model is specifically adapted for tasks related to the 'record' dataset. Developers seeking a compact, instruction-tuned model with specialized knowledge derived from this dataset may find it suitable for applications requiring focused understanding or generation within that domain.