winglian/qwen3-14b-math
The winglian/qwen3-14b-math model is a 14 billion parameter language model developed by Qwen, fine-tuned from Qwen/Qwen3-14B-Base. It is specifically optimized for mathematical reasoning and problem-solving tasks, leveraging the winglian/OpenThoughts-114k-math-correct dataset. This model is designed to enhance performance in complex mathematical contexts, making it suitable for applications requiring precise numerical and logical operations.
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Model Overview
The winglian/qwen3-14b-math is a 14 billion parameter language model, fine-tuned from the base model Qwen/Qwen3-14B-Base. This model has been specialized for mathematical tasks through training on the winglian/OpenThoughts-114k-math-correct dataset, which focuses on mathematical reasoning and problem-solving.
Key Training Details
- Base Model: Qwen/Qwen3-14B-Base
- Dataset: winglian/OpenThoughts-114k-math-correct (chat template, split thinking enabled)
- Training Framework: Built with Axolotl (version 0.10.0.dev0)
- Sequence Length: 8192 tokens, utilizing sample packing and padding.
- Optimization: Trained with
adamw_torch_fusedoptimizer, a learning rate of 1e-5, andrexscheduler over 2 epochs. - Hardware: Distributed training across 8 GPUs with a total batch size of 32.
- Performance: Achieved a validation loss of 0.3439, indicating effective learning on the mathematical dataset.
Intended Use Cases
This model is particularly well-suited for applications requiring strong mathematical capabilities, such as:
- Solving complex mathematical problems.
- Generating explanations for mathematical concepts.
- Assisting in educational tools for math.
- Developing agents that require numerical reasoning.