Model Overview

Adanato/qwen25_3b_qwen25_qwen3_rank_only-qwen25_qwen3_rank_only_cluster_0 is a specialized 3.1 billion parameter language model derived from the Qwen/Qwen2.5-3B base architecture. This model has undergone specific fine-tuning on the qwen25_qwen3_rank_only_cluster_0 dataset, indicating an optimization for tasks involving ranking or preference modeling within the Qwen 2.5 and Qwen 3 ecosystems.

Key Training Details

The model was trained using the following hyperparameters:

• Base Model: Qwen/Qwen2.5-3B
• Dataset: qwen25_qwen3_rank_only_cluster_0
• Learning Rate: 1e-05
• Epochs: 1.0
• Batch Size: 4 (train), 8 (eval) with 8 gradient accumulation steps, resulting in a total effective batch size of 128.
• Optimizer: AdamW_Torch_Fused with cosine learning rate scheduler and 0.1 warmup ratio.
• Context Length: 32768 tokens

Potential Use Cases

Given its fine-tuning on a ranking-specific dataset, this model is likely best suited for applications requiring:

• Preference Modeling: Understanding and predicting user preferences or rankings.
• Comparative Analysis: Tasks where comparing and ordering different outputs or options is crucial.
• Specialized Evaluation: Potentially useful in evaluating or scoring outputs from other Qwen2.5 or Qwen3 models based on learned ranking criteria.