alirizaercan/qwen25_05b_base_full_ft_lunarlander_a4000
The alirizaercan/qwen25_05b_base_full_ft_lunarlander_a4000 model is a 0.5 billion parameter Qwen2.5-based language model, fine-tuned by alirizaercan. It is specifically adapted from Qwen/Qwen2.5-0.5B for tasks related to the lunar_lander_270_reward_train dataset, achieving an accuracy of 0.9905 on its evaluation set. This model is optimized for specialized applications requiring high accuracy on specific, fine-tuned tasks rather than general-purpose language generation.
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Model Overview
This model, developed by alirizaercan, is a fine-tuned variant of the Qwen2.5-0.5B architecture, a compact 0.5 billion parameter language model. It has been specifically adapted for tasks related to the lunar_lander_270_reward_train dataset, demonstrating strong performance with an accuracy of 0.9905 and a loss of 0.0253 on its evaluation set.
Key Capabilities
- Specialized Task Performance: Achieves high accuracy on the
lunar_lander_270_reward_traindataset, indicating strong performance in its fine-tuned domain. - Efficient Architecture: Based on the 0.5 billion parameter Qwen2.5 model, offering a balance between performance and computational efficiency.
Training Details
The model was trained with a learning rate of 5e-06, a batch size of 1 (accumulated to 32), and utilized the AdamW optimizer with a cosine learning rate scheduler. Training involved 1.0 epoch with mixed-precision training (Native AMP), resulting in consistent improvements in validation loss and accuracy over 6000 steps.
Good For
- Specific Domain Applications: Ideal for use cases directly related to the
lunar_lander_270_reward_traindataset or similar control/reward prediction tasks. - Resource-Constrained Environments: Its small parameter count makes it suitable for deployment where computational resources are limited, while still delivering high accuracy for its specialized function.