AmberYifan/capsd-medicine-dedup-marin-8b-base-medicine_random_b10000_s0
AmberYifan/capsd-medicine-dedup-marin-8b-base-medicine_random_b10000_s0 is an 8 billion parameter language model fine-tuned from marin-community/marin-8b-base. This model is specifically adapted for medical applications, having been trained on a dedicated medical dataset. It is designed for tasks requiring specialized knowledge within the medicine domain.
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Model Overview
This model, AmberYifan/capsd-medicine-dedup-marin-8b-base-medicine_random_b10000_s0, is an 8 billion parameter language model. It is a fine-tuned version of the marin-community/marin-8b-base architecture, specifically adapted for the medical domain.
Key Characteristics
- Base Model: Fine-tuned from
marin-community/marin-8b-base. - Parameter Count: 8 billion parameters.
- Domain Specialization: Trained on the
capsd_marin-8b-base-n80000-medicine-dedup80k__mix_medicine_random_b10000_s0dataset, indicating a focus on medical text.
Training Details
The model underwent a single epoch of training with a learning rate of 1e-05 and a total batch size of 64 across 4 GPUs. The optimizer used was AdamW with cosine learning rate scheduling and a warmup of 0.03 steps. The training utilized Transformers 5.7.0, Pytorch 2.13.0+cu130, Datasets 4.0.0, and Tokenizers 0.22.2.
Intended Use
This model is intended for applications requiring specialized understanding and generation of text within the medical field, leveraging its fine-tuning on a dedicated medical dataset.