EhDa24/MNLP_M2_mcqa_model_full_ft1
EhDa24/MNLP_M2_mcqa_model_full_ft1 is a fine-tuned language model based on Qwen/Qwen3-0.6B-Base. This model was trained with a learning rate of 3e-05 over 2 epochs, utilizing an AdamW optimizer. Its specific fine-tuning dataset and primary use cases are not detailed in the available information, suggesting it's a specialized adaptation of the Qwen3-0.6B-Base architecture.
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Model Overview
EhDa24/MNLP_M2_mcqa_model_full_ft1 is a specialized language model derived from the Qwen/Qwen3-0.6B-Base architecture. It has undergone a fine-tuning process, though the specific dataset used for this training is not disclosed in the available documentation.
Training Details
The model was trained using the following key hyperparameters:
- Base Model: Qwen/Qwen3-0.6B-Base
- Learning Rate: 3e-05
- Optimizer: AdamW_TORCH with betas=(0.9, 0.999) and epsilon=1e-08
- Batch Size: 3 (train and eval), with a total effective batch size of 12 due to gradient accumulation
- Epochs: 2
- LR Scheduler: Linear type with 200 warmup steps
Current Limitations
Detailed information regarding the model's specific intended uses, limitations, and the training and evaluation data is currently unavailable. Users should be aware that its precise capabilities and optimal applications are not fully documented, requiring further investigation or experimentation for specific use cases.