AQuarterMile/WritingBench-Critic-Model-Qwen-7B
AQuarterMile/WritingBench-Critic-Model-Qwen-7B is a 7.6 billion parameter language model fine-tuned from Qwen/Qwen2.5-7B-Instruct. Developed by AQuarterMile, this model is specifically optimized for writing evaluation tasks, trained on a 50K supervised fine-tuning dataset. It functions as a critic, independently assigning scores on a 10-point scale for various criteria and providing justifications for each evaluation.
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Overview
The AQuarterMile/WritingBench-Critic-Model-Qwen-7B is a specialized language model derived from the Qwen2.5-7B-Instruct architecture. With 7.6 billion parameters, its primary function is to act as an evaluator for writing tasks, providing critical assessment of generated text.
Key Capabilities
- Writing Evaluation: Fine-tuned on a 50K supervised fine-tuning (SFT) dataset specifically for assessing written responses.
- Criterion-based Scoring: Independently assigns scores on a 10-point scale for each evaluation criterion.
- Justification Generation: Provides detailed justifications alongside each score, explaining the reasoning behind the evaluation.
- Research-backed: Part of the broader WritingBench project, as detailed in the associated paper and GitHub repository.
Training Details
The model was trained using a learning rate of 7e-06, with a total batch size of 64 (1 train_batch_size with 8 gradient_accumulation_steps across 8 devices) over 3 epochs. It utilized the AdamW_TORCH optimizer and a cosine learning rate scheduler with a 0.1 warmup ratio.
Good For
- Automated assessment of written content.
- Providing structured feedback on text generation.
- Research and development in AI-driven writing evaluation systems.