Chaitanya14/Financial_Agent

The Financial Agent is powered by FinBloom 7B, specifically fine-tuned on the Financial Context Dataset to act as a bridge between natural language and structured data. It functions by converting complex user inquiries into optimized retrieval parameters, allowing for seamless integration with downstream Data Modules. This specialized translation layer ensures that financial data sourcing is both highly precise and computationally efficient.

How to Get Started with the Model

Use the code below to get started with the model.

from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

peft_model_id = "Chaitanya14/Financial_Agent"

config = PeftConfig.from_pretrained(peft_model_id)

tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
model = PeftModel.from_pretrained(model, peft_model_id)

Citation

If you use the FinBloom 7B LLM, please cite with the following BibTex entry:

@misc{sinha2025finbloomknowledgegroundinglarge,
      title={FinBloom: Knowledge Grounding Large Language Model with Real-time Financial Data}, 
      author={Ankur Sinha and Chaitanya Agarwal and Pekka Malo},
      year={2025},
      eprint={2502.18471},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2502.18471}, 
}

• PEFT 0.10.1.dev0