AmberYifan/capsd-medcase-marin-8b-base-medicine_random_b1000_s0
The AmberYifan/capsd-medcase-marin-8b-base-medicine_random_b1000_s0 model is an 8 billion parameter language model, fine-tuned from marin-community/marin-8b-base. It is specifically adapted for medical applications, leveraging a specialized dataset for enhanced performance in the medicine domain. This model is designed for tasks requiring medical knowledge and understanding, offering a context length of 8192 tokens.
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Model Overview
AmberYifan/capsd-medcase-marin-8b-base-medicine_random_b1000_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 medical use cases through fine-tuning on the capsd_marin-8b-base-n13092-medicine-medcase__mix_medicine_random_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.
- Specialization: Optimized for tasks within the medical domain.
Training Details
The model was trained with a learning rate of 1e-05, a train_batch_size of 2, and gradient_accumulation_steps of 8, resulting in a total_train_batch_size of 64. It utilized the AdamW optimizer with a cosine learning rate scheduler and was trained for 1 epoch. The training environment included Transformers 5.7.0, Pytorch 2.13.0+cu130, Datasets 4.0.0, and Tokenizers 0.22.2.
Intended Use Cases
This model is primarily intended for applications requiring specialized medical knowledge, such as:
- Processing and understanding medical texts.
- Assisting with medical information retrieval.
- Generating medically relevant content.