AmberYifan/capsd-convfinqa-fullscore-marin-8b-base-finance_random_b8000_s0
The AmberYifan/capsd-convfinqa-fullscore-marin-8b-base-finance_random_b8000_s0 model is an 8 billion parameter language model, fine-tuned from marin-community/marin-8b-base. This model is specifically adapted for financial applications, having been trained on the capsd_marin-8b-base-n11082-finance-convfinqa-fullscore__mix_finance_random_b8000_s0 dataset. It is optimized for tasks within the finance domain, leveraging its 8192 token context length for processing financial text.
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Model Overview
This model, AmberYifan/capsd-convfinqa-fullscore-marin-8b-base-finance_random_b8000_s0, is an 8 billion parameter language model. It is a fine-tuned variant of the marin-community/marin-8b-base architecture, specifically adapted for financial applications.
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 tasks through fine-tuning on a specialized dataset.
Training Details
The model was fine-tuned using the capsd_marin-8b-base-n11082-finance-convfinqa-fullscore__mix_finance_random_b8000_s0 dataset. Key training hyperparameters included a learning rate of 1e-05, a total train batch size of 64 (with gradient accumulation steps of 8 across 4 devices), and a cosine learning rate scheduler with 0.03 warmup steps over 1 epoch. The training utilized Transformers 5.7.0 and Pytorch 2.13.0+cu130.
Intended Use Cases
This model is primarily intended for applications requiring strong performance in the financial domain, given its specialized training data. Its fine-tuning on a finance-specific dataset suggests suitability for tasks such as financial text analysis, question answering in finance, or other domain-specific language understanding tasks.