Model Overview

This model, rbelanec/train_qnli_42_1773765556, is a specialized 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.

Key Capabilities

• Question-answering Natural Language Inference: The model is fine-tuned to determine if a given text snippet contains the answer to a question, or if it supports or contradicts a hypothesis based on a question.
• Performance: Achieved a validation loss of 0.1074 on the QNLI evaluation set after 5 training epochs, indicating strong performance on its target task.
• Training Details: Trained with a learning rate of 5e-05, batch sizes of 8, and utilized the AdamW optimizer with a cosine learning rate scheduler.

When to Use This Model

• QNLI Tasks: Ideal for applications requiring high accuracy in natural language inference related to question-answering.
• Resource-Efficient Inference: As a 1 billion parameter model, it offers a more lightweight solution compared to larger models while still providing specialized performance for QNLI.