rberberi/qwen3-finetuned
The rberberi/qwen3-finetuned model is a 0.8 billion parameter language model, fine-tuned by rberberi from the Qwen/Qwen3-0.6B base model. It features a 32768 token context length. This model is a specialized iteration of the Qwen3 architecture, with its specific fine-tuning dataset and primary use case currently undefined, as indicated by the original model card.
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Overview
This model, rberberi/qwen3-finetuned, is a fine-tuned version of the Qwen/Qwen3-0.6B base model. It has approximately 0.8 billion parameters and supports a 32768 token context length. The fine-tuning process involved an unspecified dataset, and the model achieved a validation loss of 1.2067 after 3 epochs.
Training Details
The model was trained using the following key hyperparameters:
- Learning Rate: 2e-05
- Optimizer: ADAMW_TORCH_FUSED
- Batch Size: 16 (total, with gradient accumulation)
- Epochs: 3
- Mixed Precision Training: Native AMP
Current Status
As per the provided model card, more information is needed regarding its specific intended uses, limitations, and the details of the training and evaluation data. Developers should note that the exact differentiators and optimal use cases for this fine-tuned version are not yet fully documented.