rbelanec/train_record_42_1779354541
The rbelanec/train_record_42_1779354541 model is a 1 billion parameter instruction-tuned causal language model, fine-tuned by rbelanec from the meta-llama/Llama-3.2-1B-Instruct base model. It was trained on the 'record' dataset, achieving a validation loss of 0.3557 over 49 million input tokens seen. This model is specialized for tasks related to the 'record' dataset, demonstrating improved performance in that specific domain.
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Model Overview
The rbelanec/train_record_42_1779354541 model is a specialized 1 billion parameter language model, fine-tuned by rbelanec. It is based on the meta-llama/Llama-3.2-1B-Instruct architecture, indicating its foundation in the Llama 3.2 series and its instruction-following capabilities.
Key Characteristics
- Base Model: Fine-tuned from
meta-llama/Llama-3.2-1B-Instruct. - Parameter Count: 1 billion parameters.
- Context Length: Supports a context length of 32768 tokens.
- Training Data: Fine-tuned specifically on the 'record' dataset.
- Performance: Achieved a validation loss of 0.3557 during training, with approximately 49 million input tokens processed.
Training Details
The model underwent a single epoch of training using a learning rate of 2e-06, a batch size of 8 for both training and evaluation, and an AdamW optimizer. A cosine learning rate scheduler with a 0.1 warmup ratio was employed. The training process showed a consistent reduction in validation loss, indicating effective learning on the target dataset.
Intended Use Cases
This model is primarily intended for applications and research focused on tasks related to the 'record' dataset, given its specific fine-tuning. Its relatively small size (1B parameters) combined with a large context window makes it suitable for applications requiring efficient processing of long sequences within its specialized domain.