KulpreetKaurS/qwen3-finetuned
KulpreetKaurS/qwen3-finetuned is a 0.8 billion parameter causal language model, fine-tuned from Qwen/Qwen3-0.6B. This model was trained for 1 epoch with a learning rate of 2e-05 and a total batch size of 256. It achieved a validation loss of 3.2427, indicating its performance on the fine-tuning task.
Loading preview...
Model Overview
KulpreetKaurS/qwen3-finetuned is a 0.8 billion parameter language model derived from the Qwen3-0.6B architecture. This model has undergone a fine-tuning process, although the specific dataset used for this training is not detailed in the available information. The fine-tuning aimed to adapt the base Qwen3 model to a particular task, as evidenced by its training loss and validation loss metrics.
Training Details
The model was trained for a single epoch using a learning rate of 2e-05. Key hyperparameters included a train_batch_size of 16, eval_batch_size of 8, and gradient_accumulation_steps of 16, resulting in an effective total_train_batch_size of 256. The optimizer used was ADAMW_TORCH_FUSED with standard beta values and epsilon. During training, the model achieved a validation loss of 3.2427.
Potential Use Cases
Given its fine-tuned nature, this model could be suitable for tasks similar to those in its (undisclosed) training data. Developers might consider it for:
- Specific text generation tasks where a smaller, fine-tuned model is preferred for efficiency.
- Research and experimentation with fine-tuning Qwen3-0.6B on custom datasets.
Further evaluation and understanding of the fine-tuning dataset would be necessary to determine its optimal applications and limitations.