minsu0567/Uni-IAD-R2-Qwen3.5_2
The minsu0567/Uni-IAD-R2-Qwen3.5_2 is a 4.5 billion parameter language model, fine-tuned from unsloth/Qwen3.5-4B. This model is specifically adapted using the PA_SFT_2_reordered dataset, suggesting an optimization for tasks related to its training data. It is designed for applications requiring a moderately sized, specialized Qwen3.5-based model.
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Model Overview
This model, minsu0567/Uni-IAD-R2-Qwen3.5_2, is a 4.5 billion parameter language model. It is a fine-tuned variant of the unsloth/Qwen3.5-4B base model, indicating its foundation in the Qwen3.5 architecture.
Key Characteristics
- Base Model: Derived from
unsloth/Qwen3.5-4B. - Parameter Count: 4.5 billion parameters, offering a balance between performance and computational efficiency.
- Training Data: Fine-tuned on the
PA_SFT_2_reordereddataset, suggesting specialized capabilities aligned with this data. - Training Hyperparameters: Utilized a learning rate of 1e-05, a batch size of 1 with 2 gradient accumulation steps, and a cosine learning rate scheduler over 1 epoch.
Potential Use Cases
Given its fine-tuning on a specific dataset, this model is likely suitable for:
- Applications requiring a Qwen3.5-based model with adaptations from the
PA_SFT_2_reordereddataset. - Tasks where a 4.5B parameter model provides sufficient performance without the overhead of larger models.
Further details on intended uses and limitations would require more information about the PA_SFT_2_reordered dataset and specific evaluation results.