Model Overview

The rbelanec/train_qqp_42_1773765557 model is a specialized 1 billion parameter language model, fine-tuned by rbelanec. It is built upon the meta-llama/Llama-3.2-1B-Instruct architecture, indicating its foundation in a Llama-family instruction-tuned model.

Key Capabilities

• Question Pair Similarity: The model has been specifically fine-tuned on the QQP (Quora Question Pairs) dataset, making it adept at tasks requiring the identification of semantic equivalence between two questions.
• Performance Metrics: During training, it achieved a validation loss of 0.1541, with a total of 137,941,664 input tokens seen across 5 epochs.

Training Details

The training process utilized a learning rate of 5e-05, a batch size of 8, and the ADAMW_TORCH optimizer. A cosine learning rate scheduler with a warmup ratio of 0.1 was employed over 5 epochs. The model was developed using Transformers 4.51.3, Pytorch 2.10.0+cu128, Datasets 4.0.0, and Tokenizers 0.21.4.

Intended Use Cases

This model is primarily suited for applications requiring high accuracy in determining if two questions are semantically the same, such as:

• Duplicate Question Detection: Identifying redundant questions in forums or Q&A platforms.
• Information Retrieval: Improving search relevance by grouping similar queries.
• Customer Support: Routing similar customer inquiries to appropriate resources.