AmanKhan2002/qwen3-finetuned
AmanKhan2002/qwen3-finetuned is a 0.8 billion parameter causal language model, fine-tuned by AmanKhan2002 from the Qwen3-0.6B architecture. This model was trained for 1 epoch with a learning rate of 2e-05 and achieved a validation loss of 3.2387. Its specific intended uses and primary differentiators are not detailed in the available information.
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Model Overview
This model, AmanKhan2002/qwen3-finetuned, is a fine-tuned variant of the Qwen3-0.6B base model developed by Qwen. It features approximately 0.8 billion parameters and was trained for a single epoch. The training process utilized a learning rate of 2e-05 with an AdamW optimizer, a batch size of 16, and a gradient accumulation of 16 steps, resulting in a total training batch size of 256. During evaluation, it achieved a validation loss of 3.2387.
Key Characteristics
- Base Model: Qwen3-0.6B
- Parameter Count: ~0.8 billion
- Training Epochs: 1
- Validation Loss: 3.2387
- Frameworks: Built with Transformers 5.13.1, Pytorch 2.11.0+cu128, Datasets 4.0.0, and Tokenizers 0.22.2.
Intended Uses & Limitations
The specific intended uses, detailed capabilities, and limitations of this fine-tuned model are not explicitly provided in the available documentation. Further information would be needed to determine optimal applications or potential constraints.