akshay-sked/qwen3-14b-svamp-sft
The akshay-sked/qwen3-14b-svamp-sft model is a 14 billion parameter language model, fine-tuned from Qwen/Qwen3-14B on the svamp_final_sft_train dataset. This model is specifically optimized for mathematical reasoning tasks, demonstrating a validation loss of 0.2875 after one epoch of training. It is designed to enhance performance in problem-solving scenarios requiring numerical and logical understanding.
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Model Overview
The akshay-sked/qwen3-14b-svamp-sft is a 14 billion parameter language model, derived from the Qwen/Qwen3-14B architecture. This model has undergone specific fine-tuning on the svamp_final_sft_train dataset, indicating an optimization focus on mathematical word problems and similar reasoning tasks.
Key Training Details
- Base Model: Qwen/Qwen3-14B
- Dataset:
svamp_final_sft_train - Training Epochs: 1.0
- Validation Loss: Achieved 0.2875 at the end of training.
- Learning Rate: 5e-06
- Optimizer: ADAMW_TORCH_FUSED
- Batch Size: A total train batch size of 8 (with gradient accumulation steps of 8).
Potential Use Cases
Given its fine-tuning on a dataset related to mathematical problem-solving, this model is likely best suited for:
- Mathematical Reasoning: Tasks involving numerical understanding, arithmetic, and logical deduction from problem statements.
- Educational Applications: Generating solutions or explanations for math problems.
- Data Analysis Support: Assisting with tasks that require interpreting quantitative information.
Limitations
The model card indicates that more information is needed regarding its full description, intended uses, limitations, and specific training/evaluation data. Users should exercise caution and conduct thorough testing for critical applications, as detailed performance characteristics beyond the reported loss are not yet available.