AmberYifan/capsd-marin-8b-base-math_qurating_b2000_s0
AmberYifan/capsd-marin-8b-base-math_qurating_b2000_s0 is an 8 billion parameter language model, fine-tuned from marin-community/marin-8b-base. This model is specifically optimized for mathematical reasoning tasks, having been trained on the capsd_marin-8b-base-n80000-numina__mix_math_qurating_b2000_s0 dataset. It is designed to enhance performance in mathematical problem-solving and related quantitative applications.
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Model Overview
This model, AmberYifan/capsd-marin-8b-base-math_qurating_b2000_s0, is an 8 billion parameter language model derived from the marin-community/marin-8b-base architecture. It has been specifically fine-tuned to improve its capabilities in mathematical reasoning and problem-solving.
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 training on the
capsd_marin-8b-base-n80000-numina__mix_math_qurating_b2000_s0dataset.
Training Details
The model underwent a fine-tuning process with a learning rate of 1e-05, a train_batch_size of 2, and a gradient_accumulation_steps of 8, resulting in a total_train_batch_size of 64. It was trained for 1 epoch using the AdamW optimizer with a cosine learning rate scheduler.
Intended Use Cases
This model is particularly suitable for applications requiring strong mathematical understanding and problem-solving abilities. Developers can leverage its specialized training for tasks such as:
- Solving mathematical equations.
- Generating explanations for mathematical concepts.
- Assisting in quantitative analysis.
- Developing educational tools focused on mathematics.