rbelanec/train_boolq_42_1776331558
The rbelanec/train_boolq_42_1776331558 model is a 1 billion parameter language model fine-tuned from meta-llama/Llama-3.2-1B-Instruct. It is specifically optimized for Boolean question answering tasks, having been trained on the BoolQ dataset. This model demonstrates a validation loss of 0.1885 on the evaluation set, making it suitable for applications requiring precise true/false responses to natural language queries.
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Model Overview
This model, rbelanec/train_boolq_42_1776331558, is a 1 billion parameter language model derived from meta-llama/Llama-3.2-1B-Instruct. It has been specifically fine-tuned on the BoolQ dataset to excel at Boolean question answering, where the task is to determine if a given passage supports a question that can be answered with 'yes' or 'no'.
Key Characteristics
- Base Model: Fine-tuned from Llama-3.2-1B-Instruct.
- Parameter Count: 1 billion parameters.
- Context Length: Supports a context length of 32768 tokens.
- Specialization: Optimized for Boolean question answering tasks.
- Performance: Achieved a validation loss of 0.1885 on the evaluation set after training.
Training Details
The model was trained for 5 epochs with a learning rate of 5e-06 and a batch size of 8. The training utilized the AdamW optimizer with a cosine learning rate scheduler and a warmup ratio of 0.1. The training process involved processing over 12 million input tokens.
Intended Use Cases
This model is particularly well-suited for applications requiring accurate binary (yes/no) answers to questions based on provided text. It can be integrated into systems for:
- Information Retrieval: Quickly verifying facts or statements.
- Content Moderation: Identifying content that aligns or conflicts with specific criteria.
- Automated Customer Support: Answering straightforward factual questions.