minsu0567/Uni-IAD-R2-Qwen3.5-answer-last2
The minsu0567/Uni-IAD-R2-Qwen3.5-answer-last2 model 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_answer_last2 dataset. It is designed for tasks requiring focused response generation based on its specialized training data. The model leverages a 32768 token context length for processing longer inputs.
Loading preview...
Model Overview
The minsu0567/Uni-IAD-R2-Qwen3.5-answer-last2 is a 4.5 billion parameter language model, fine-tuned from the unsloth/Qwen3.5-4B base model. This specialization was achieved through training on the PA_SFT_2_answer_last2 dataset, indicating an optimization for specific answer generation tasks.
Key Characteristics
- Base Model: Fine-tuned from unsloth/Qwen3.5-4B.
- Parameter Count: 4.5 billion parameters.
- Context Length: Supports a 32768 token context window.
- Training Data: Specialized on the PA_SFT_2_answer_last2 dataset, suggesting a focus on particular response formats or content.
Training Details
The model was trained with a learning rate of 1e-05, a batch size of 1 (with 2 gradient accumulation steps), and 1 epoch. The optimizer used was ADAMW_BNB with standard betas and epsilon, and a cosine learning rate scheduler with 100 warmup steps. This configuration aims to adapt the base model efficiently to the target dataset.
Intended Use Cases
Given its fine-tuning on a specific 'answer_last2' dataset, this model is likely best suited for applications where the goal is to generate precise or contextually relevant answers based on the patterns learned from its training data. Developers should evaluate its performance on tasks that align with the characteristics of the PA_SFT_2_answer_last2 dataset.