rbelanec/train_qnli_42_1779286680
The rbelanec/train_qnli_42_1779286680 model is a 1 billion parameter language model fine-tuned from meta-llama/Llama-3.2-1B-Instruct. It is specifically optimized for question-answering tasks, having been fine-tuned on the QNLI dataset. This model demonstrates a validation loss of 0.0523, indicating its specialization in natural language inference for question answering.
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Overview
This model, rbelanec/train_qnli_42_1779286680, is a 1 billion parameter language model derived from meta-llama/Llama-3.2-1B-Instruct. It has undergone fine-tuning specifically on the QNLI (Question-answering Natural Language Inference) dataset, making it specialized for tasks involving natural language inference in a question-answering context.
Key Capabilities
- Question Answering (QNLI): Optimized for determining if a given text snippet contains the answer to a question, as evidenced by its fine-tuning on the QNLI dataset.
- Low Validation Loss: Achieved a final validation loss of 0.0523, suggesting good performance on its target task.
Training Details
The model was trained for 1 epoch with a learning rate of 2e-06 and a batch size of 8. The optimizer used was AdamW with cosine learning rate scheduling and a warmup ratio of 0.1. The training process involved processing over 11 million input tokens.
Intended Uses
This model is best suited for applications requiring precise natural language inference for question-answering, particularly where the task involves determining entailment or contradiction between a question and a context sentence. Its smaller size (1B parameters) makes it suitable for deployment in environments with computational constraints, while its specialized fine-tuning aims for efficiency in its specific domain.