AmberYifan/capsd-convfinqa-fullscore-marin-8b-base-finance_cap_b1000_s0

TEXT GENERATIONPricing:Input $0.37 / Cached $0.074 / Output $0.38Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Aug 16, 2026License:otherArchitecture:Transformer Featherless Exclusive Cold

AmberYifan/capsd-convfinqa-fullscore-marin-8b-base-finance_cap_b1000_s0 is an 8 billion parameter language model, fine-tuned from marin-community/marin-8b-base. This model is specifically adapted for financial question answering, leveraging the capsd_marin-8b-base-n11082-finance-convfinqa-fullscore__mix_finance_cap_b1000_s0 dataset. It is designed to excel in conversational financial contexts, making it suitable for applications requiring specialized financial understanding.

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Model Overview

This model, AmberYifan/capsd-convfinqa-fullscore-marin-8b-base-finance_cap_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 applications.

Key Capabilities

  • Financial Question Answering: The model has been fine-tuned on the capsd_marin-8b-base-n11082-finance-convfinqa-fullscore__mix_finance_cap_b1000_s0 dataset, indicating a specialization in understanding and responding to financial queries.
  • Conversational Finance: Its training on a "convfinqa" (conversational financial question answering) dataset suggests proficiency in handling interactive financial discussions.

Training Details

The model underwent a single epoch of training with a learning rate of 1e-05 and a total batch size of 64 (achieved with a train_batch_size of 2 and gradient_accumulation_steps of 8). It utilized the AdamW optimizer with a cosine learning rate scheduler.

Intended Use Cases

This model is primarily intended for use cases requiring specialized financial knowledge and conversational abilities, such as:

  • Financial chatbots
  • Automated financial advisory systems
  • Information retrieval from financial documents through natural language queries

Limitations

The model card indicates that more information is needed regarding its specific limitations and broader intended uses, suggesting users should conduct thorough evaluations for their particular applications.

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