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. This model has a context length of 32768 tokens. While the specific fine-tuning dataset is unknown, its primary differentiator is its fine-tuned nature, suggesting optimization for specific, undisclosed tasks beyond the base model's general capabilities. It is intended for use cases that benefit from a compact, fine-tuned model with a substantial context window.
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Overview
The rberberi/qwen3-finetuned model is a specialized version of the Qwen3-0.6B base model, developed by rberberi. This model, with approximately 0.8 billion parameters, has been fine-tuned on an unspecified dataset, indicating a focus on particular tasks or domains not covered by the original base model. It supports a substantial context length of 32768 tokens, making it suitable for processing longer inputs.
Key Characteristics
- Base Model: Fine-tuned from Qwen/Qwen3-0.6B.
- Parameter Count: Approximately 0.8 billion parameters.
- Context Length: Supports a large context window of 32768 tokens.
- Fine-tuned: Optimized through fine-tuning, though the specific dataset and target tasks are not detailed in the provided information.
Training Details
The model was trained with a learning rate of 2e-05, a total batch size of 16 (achieved with train_batch_size: 2 and gradient_accumulation_steps: 8), and for 3 epochs. Mixed-precision training (Native AMP) was utilized. The training process resulted in a final validation loss of 1.2141.
Good for
- Applications requiring a compact, fine-tuned language model.
- Tasks benefiting from a large context window (32768 tokens).
- Exploration of fine-tuned Qwen3 variants for specific, custom use cases.