AmberYifan/capsd-qwen3-numina-Qwen3-4B-Base-math_random_b1000_s0
AmberYifan/capsd-qwen3-numina-Qwen3-4B-Base-math_random_b1000_s0 is a 4 billion parameter language model, fine-tuned from Qwen/Qwen3-4B-Base. This model is specifically optimized for mathematical reasoning tasks, leveraging a specialized dataset for its training. It is designed to enhance performance in numerical and logical problem-solving contexts, making it suitable for applications requiring robust mathematical capabilities.
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Model Overview
This model, AmberYifan/capsd-qwen3-numina-Qwen3-4B-Base-math_random_b1000_s0, is a fine-tuned variant of the Qwen3-4B-Base architecture. It has been specifically adapted using the capsd_Qwen3-4B-Base-n80000-numina__mix_math_random_b1000_s0 dataset, indicating a focus on mathematical reasoning and problem-solving.
Key Training Details
- Base Model: Qwen/Qwen3-4B-Base
- Learning Rate: 1e-05
- Batch Size: 2 (train), 8 (eval)
- Gradient Accumulation Steps: 8
- Optimizer: ADAMW_TORCH
- LR Scheduler: Cosine with 0.03 warmup steps
- Epochs: 1
Potential Use Cases
Given its fine-tuning on a math-related dataset, this model is likely best suited for:
- Mathematical Problem Solving: Tasks involving arithmetic, algebra, or other numerical reasoning.
- Data Analysis: Generating insights or performing calculations based on structured data.
- Educational Tools: Assisting with math homework or generating practice problems.
Further details on specific intended uses and limitations would require more information from the original developers.