AmberYifan/capsd-marin-8b-base-math_dsir_b1000_s0
AmberYifan/capsd-marin-8b-base-math_dsir_b1000_s0 is an 8 billion parameter language model, fine-tuned from marin-community/marin-8b-base. This model is specifically trained on the capsd_marin-8b-base-n80000-numina__mix_math_dsir_b1000_s0 dataset, suggesting an optimization for mathematical reasoning and related tasks. With a context length of 8192 tokens, it is designed for applications requiring specialized mathematical processing.
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Model Overview
AmberYifan/capsd-marin-8b-base-math_dsir_b1000_s0 is an 8 billion parameter language model, fine-tuned from the marin-community/marin-8b-base architecture. This model has been specifically trained on the capsd_marin-8b-base-n80000-numina__mix_math_dsir_b1000_s0 dataset.
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.
- Training Data: Specialized training on a dataset with "math_dsir" in its name, indicating a focus on mathematical domains.
Training Details
The model was trained using the following hyperparameters:
- Learning Rate: 1e-05
- Optimizer: ADAMW_TORCH
- Batch Size: A total train batch size of 64 (2 per device with 8 gradient accumulation steps across 4 GPUs).
- Epochs: Trained for 1 epoch.
Potential Use Cases
Given its specialized training, this model is likely suitable for:
- Mathematical problem-solving.
- Reasoning tasks involving numerical data.
- Applications requiring understanding and generation of mathematical content.