AmberYifan/capsd-finance-dedup-marin-8b-base-finance_random_b2000_s0
AmberYifan/capsd-finance-dedup-marin-8b-base-finance_random_b2000_s0 is an 8 billion parameter language model fine-tuned from marin-community/marin-8b-base. This model is specifically optimized for financial applications, having been trained on the capsd_marin-8b-base-n80000-finance-dedup80k__mix_finance_random_b2000_s0 dataset. Its primary strength lies in processing and generating content relevant to financial contexts, making it suitable for specialized financial language tasks. The model leverages a context length of 8192 tokens to handle extensive financial documents.
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Model Overview
This model, AmberYifan/capsd-finance-dedup-marin-8b-base-finance_random_b2000_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 financial domain tasks.
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.
- Domain Specialization: Optimized for financial applications through specialized training.
Training Details
The model was trained using the capsd_marin-8b-base-n80000-finance-dedup80k__mix_finance_random_b2000_s0 dataset. Key training hyperparameters included 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. The training utilized an AdamW optimizer with a cosine learning rate scheduler over 1 epoch. The training environment included Transformers 5.7.0 and Pytorch 2.13.0+cu130.
Intended Use Cases
This model is best suited for applications requiring deep understanding and generation of financial text. Its fine-tuning on a finance-specific dataset suggests improved performance on tasks such as financial analysis, report generation, and processing financial documents compared to general-purpose models.