AmberYifan/capsd-marin-8b-base-math_kcenter_b8000_s0
AmberYifan/capsd-marin-8b-base-math_kcenter_b8000_s0 is an 8 billion parameter language model, fine-tuned from marin-community/marin-8b-base. This model is specifically optimized for mathematical tasks, leveraging a specialized dataset for its training. It is designed to enhance performance in numerical reasoning and problem-solving within an 8192-token context window. The model aims to provide improved accuracy for applications requiring strong mathematical capabilities.
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Model Overview
AmberYifan/capsd-marin-8b-base-math_kcenter_b8000_s0 is an 8 billion parameter language model, fine-tuned from the marin-community/marin-8b-base architecture. This iteration focuses on enhancing mathematical reasoning capabilities through specialized training.
Key Characteristics
- Base Model: Fine-tuned from
marin-community/marin-8b-base. - Parameter Count: 8 billion parameters.
- Context Length: Supports an 8192-token context window.
- Specialization: Optimized for mathematical tasks, trained on the
capsd_marin-8b-base-n80000-numina__mix_math_kcenter_b8000_s0dataset.
Training Details
The model was trained with a learning rate of 1e-05, a total batch size of 64 (across 4 GPUs with 8 gradient accumulation steps), and utilized the AdamW optimizer with a cosine learning rate scheduler. Training consisted of 1 epoch. The development environment included Transformers 5.7.0, Pytorch 2.13.0+cu130, Datasets 4.0.0, and Tokenizers 0.22.2.
Intended Use Cases
This model is particularly suited for applications requiring robust mathematical problem-solving and numerical understanding. Its fine-tuning on a math-centric dataset suggests improved performance in areas such as:
- Mathematical reasoning.
- Solving arithmetic and algebraic problems.
- Generating mathematically accurate responses.