AmberYifan/capsd-marin-8b-base-math_kcenter_b2000_s0

Model Overview

The AmberYifan/capsd-marin-8b-base-math_kcenter_b2000_s0 is an 8 billion parameter language model, fine-tuned from the marin-community/marin-8b-base architecture. This model has been specifically adapted for enhanced performance in mathematical domains.

Key Capabilities

• Mathematical Reasoning: Optimized through fine-tuning on a specialized dataset, making it suitable for tasks involving mathematical problem-solving and understanding.
• Base Model: Built upon the marin-8b-base model, suggesting a strong foundation for general language understanding prior to its mathematical specialization.
• Context Length: Supports an 8192-token context window, allowing for processing and generating longer sequences of text, which can be beneficial for complex mathematical problems requiring detailed context.

Training Details

The model was fine-tuned using the capsd_marin-8b-base-n80000-numina__mix_math_kcenter_b2000_s0 dataset. Key training hyperparameters included a learning rate of 1e-05, a total batch size of 64 (with gradient accumulation), and a cosine learning rate scheduler with 0.03 warmup steps over 1 epoch. The training utilized Transformers 5.7.0 and Pytorch 2.13.0+cu130.

When to Use This Model

This model is particularly well-suited for applications that require:

• Mathematical Problem Solving: Ideal for tasks where accurate mathematical computation and reasoning are critical.
• Specialized Domain Applications: Use cases that benefit from a model specifically trained on mathematical datasets, potentially outperforming general-purpose models in this area.