AmberYifan/capsd-convfinqa-fullscore-marin-8b-base-finance_cap_b2000_s0
AmberYifan/capsd-convfinqa-fullscore-marin-8b-base-finance_cap_b2000_s0 is an 8 billion parameter language model fine-tuned from marin-community/marin-8b-base. This model is specifically adapted for financial conversational question answering tasks, leveraging the capsd_marin-8b-base-n11082-finance-convfinqa-fullscore__mix_finance_cap_b2000_s0 dataset. With an 8192-token context length, it is optimized for processing and responding to finance-related queries.
Loading preview...
Model Overview
This model, AmberYifan/capsd-convfinqa-fullscore-marin-8b-base-finance_cap_b2000_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 specialized applications.
Key Characteristics
- Base Model: Fine-tuned from
marin-community/marin-8b-base. - Parameter Count: Features 8 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports an 8192-token context window, suitable for processing moderately long inputs.
- Training Data: Fine-tuned on the
capsd_marin-8b-base-n11082-finance-convfinqa-fullscore__mix_finance_cap_b2000_s0dataset, indicating a specialization in financial conversational question answering.
Training Details
The model underwent a single epoch of training with a learning rate of 1e-05. It utilized a total batch size of 64 (achieved with a train_batch_size of 2 and gradient_accumulation_steps of 8 across 4 GPUs). The optimizer used was ADAMW_TORCH with a cosine learning rate scheduler.
Potential Use Cases
Given its fine-tuning on a financial dataset, this model is likely suitable for:
- Financial question answering systems.
- Conversational AI in finance.
- Processing and extracting information from financial texts.