AmberYifan/capsd-convfinqa-fullscore-marin-8b-base-finance_ppl_b1000_s0
AmberYifan/capsd-convfinqa-fullscore-marin-8b-base-finance_ppl_b1000_s0 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_ppl_b1000_s0 dataset. It is designed for tasks requiring financial domain understanding, leveraging its 8192 token context length.
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Model Overview
This model, AmberYifan/capsd-convfinqa-fullscore-marin-8b-base-finance_ppl_b1000_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 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 following hyperparameters:
- Learning Rate: 1e-05
- Batch Size: A total training batch size of 64 (2 per device across 4 GPUs with 8 gradient accumulation steps).
- Optimizer: ADAMW_TORCH with default betas and epsilon.
- Scheduler: Cosine learning rate scheduler with 0.03 warmup steps.
- Epochs: Trained for 1 epoch.
Intended Use
While specific intended uses and limitations require further information, its training on a financial dataset suggests suitability for tasks within the finance sector.