rhaunschild/qwen3-finetuned
The rhaunschild/qwen3-finetuned model is a fine-tuned version of the Qwen/Qwen3-0.6B architecture, featuring 0.8 billion parameters and a context length of 32768 tokens. This model has undergone further training on an unspecified dataset, achieving a validation loss of 2.3994. Its specific differentiators and primary use cases are not detailed in the available information, suggesting it is a general-purpose fine-tuned variant of the Qwen3-0.6B base model.
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Model Overview
This model, rhaunschild/qwen3-finetuned, is a fine-tuned iteration of the Qwen/Qwen3-0.6B base model. It has 0.8 billion parameters and supports a context length of 32768 tokens. The fine-tuning process involved an undisclosed dataset, resulting in a final validation loss of 2.3994.
Training Details
The model was trained for 3 epochs using the following key hyperparameters:
- Learning Rate: 2e-05
- Batch Size: 8 (train), 32 (eval)
- Gradient Accumulation Steps: 8
- Optimizer: AdamW_Torch_Fused
Performance Metrics
During training, the model achieved a validation loss of 2.3994. Specific benchmarks or detailed performance characteristics beyond this loss value are not provided.
Limitations and Intended Uses
Detailed information regarding the model's intended uses, specific capabilities, or known limitations is not available in the provided documentation. Users should exercise caution and conduct further evaluation to determine its suitability for particular applications, especially given the unknown nature of the fine-tuning dataset.