KhaledAshraf/qwen3-finetuned
KhaledAshraf/qwen3-finetuned is a fine-tuned version of the Qwen3-0.6B causal language model, developed by KhaledAshraf. This compact model, with 0.8 billion parameters and a 32768-token context length, was fine-tuned on an unspecified dataset. It demonstrates a validation loss of 2.0026, indicating its performance on the evaluation set. This model is suitable for applications requiring a smaller, efficient language model derived from the Qwen3 architecture.
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Model Overview
KhaledAshraf/qwen3-finetuned is a specialized language model derived from the Qwen3-0.6B architecture. This version has been fine-tuned by KhaledAshraf, resulting in a model with 0.8 billion parameters and a substantial 32768-token context length. The fine-tuning process involved an unspecified dataset, and the model achieved a validation loss of 2.0026 on its evaluation set.
Key Characteristics
- Base Model: Qwen/Qwen3-0.6B
- Parameter Count: 0.8 billion
- Context Length: 32768 tokens
- Training Objective: Achieved a final validation loss of 2.0026 after 3 epochs.
- Training Hyperparameters: Utilized a learning rate of 2e-05, a train batch size of 2 (total batch size 16 with gradient accumulation), and the AdamW_TORCH_FUSED optimizer.
Intended Use Cases
Given its compact size and fine-tuned nature, this model is suitable for:
- Applications requiring a smaller, efficient language model.
- Scenarios where the Qwen3 architecture is preferred, but a custom fine-tuning is beneficial.
- Further experimentation or fine-tuning on specific, domain-relevant datasets.