AmberYifan/capsd-marin-8b-base-math_qurating_b1000_s0
AmberYifan/capsd-marin-8b-base-math_qurating_b1000_s0 is an 8 billion parameter language model, fine-tuned from marin-community/marin-8b-base. This model is specifically optimized for mathematical tasks, having been trained on the capsd_marin-8b-base-n80000-numina__mix_math_qurating_b1000_s0 dataset. It leverages a context length of 8192 tokens and is designed for applications requiring strong mathematical reasoning capabilities.
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Model Overview
This model, AmberYifan/capsd-marin-8b-base-math_qurating_b1000_s0, is an 8 billion parameter language model. It is a fine-tuned variant of the marin-community/marin-8b-base architecture, specifically adapted for enhanced performance in mathematical domains.
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 through fine-tuning on a specialized dataset.
Training Details
The model underwent a single epoch of training with a learning rate of 1e-05, utilizing a cosine learning rate scheduler. Training was performed with a total batch size of 64 across 4 GPUs, employing AdamW_Torch as the optimizer. The fine-tuning process focused on the capsd_marin-8b-base-n80000-numina__mix_math_qurating_b1000_s0 dataset.
Intended Use Cases
This model is particularly suitable for applications requiring robust mathematical problem-solving and reasoning. Its fine-tuning on a math-specific dataset suggests improved accuracy and understanding in numerical and logical tasks compared to general-purpose models of similar size.