AmberYifan/capsd-qwen3-numina-Qwen3-4B-Base-math_ppl_b8000_s0
The AmberYifan/capsd-qwen3-numina-Qwen3-4B-Base-math_ppl_b8000_s0 model is a 4 billion parameter Qwen3-Base variant, fine-tuned by AmberYifan. This model is specifically optimized for mathematical tasks, having been trained on the capsd_Qwen3-4B-Base-n80000-numina__mix_math_ppl_b8000_s0 dataset. It leverages a 32768 token context length, making it suitable for processing extensive mathematical problems and related text. Its specialized training aims to enhance performance in quantitative reasoning and problem-solving.
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Model Overview
This model, named capsd-qwen3-numina-Qwen3-4B-Base-math_ppl_b8000_s0, is a specialized fine-tuned version of the Qwen/Qwen3-4B-Base architecture. Developed by AmberYifan, it features 4 billion parameters and supports a substantial 32768 token context length.
Key Specialization
The primary differentiator of this model is its fine-tuning on the capsd_Qwen3-4B-Base-n80000-numina__mix_math_ppl_b8000_s0 dataset. This targeted training indicates an optimization for:
- Mathematical problem-solving: Enhanced performance on tasks requiring quantitative reasoning.
- Numerical processing: Improved understanding and generation of mathematical content.
Training Details
The model underwent a single epoch of training with a learning rate of 1e-05, utilizing a total batch size of 64 across 4 GPUs. The training employed an AdamW optimizer with a cosine learning rate scheduler, indicating a focus on stable and effective convergence for its specialized task.
When to Consider This Model
This model is particularly suited for applications where robust mathematical understanding and generation are critical, such as:
- Educational tools for math assistance.
- Scientific research requiring numerical analysis.
- Any use case demanding high accuracy in mathematical contexts.