waqarahmad2904/qwen3-finetuned
The waqarahmad2904/qwen3-finetuned model is a 0.8 billion parameter language model, fine-tuned from the Qwen/Qwen3-0.6B architecture. It was trained with a learning rate of 2e-05 over one epoch, achieving a validation loss of 3.2443. This model is a specialized iteration of the Qwen3 series, intended for tasks aligned with its fine-tuning dataset, though specific use cases require further information.
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Model Overview
The waqarahmad2904/qwen3-finetuned model is a specialized version of the Qwen/Qwen3-0.6B architecture, featuring approximately 0.8 billion parameters and a context length of 32768 tokens. This model has undergone a single epoch of fine-tuning, resulting in a validation loss of 3.2443.
Training Details
The fine-tuning process utilized the following key hyperparameters:
- Learning Rate: 2e-05
- Batch Size: 16 (train), 8 (eval)
- Gradient Accumulation Steps: 16, leading to a total effective batch size of 256
- Optimizer: ADAMW_TORCH_FUSED
- Epochs: 1
Key Characteristics
As a fine-tuned variant of the Qwen3 series, this model inherits the foundational capabilities of its base architecture. However, the specific nature of its fine-tuning dataset, which is currently unspecified, dictates its primary strengths and intended applications. Developers should consider its 0.8B parameter count for applications requiring a balance between performance and computational efficiency.
Usage Considerations
Given the limited information regarding the fine-tuning dataset and intended uses, users are advised to conduct thorough evaluations for their specific applications. The reported validation loss of 3.2443 provides a baseline metric for its performance on the evaluation set used during training.