Chaitanya14/FinBloom_7B

FinBloom 7B is a specialized Large Language Model engineered for high-performance financial NLP applications. Built upon the BLOOM-7B architecture, the model has undergone extensive fine-tuning using a robust corpus of historical financial data, including comprehensive news archives from Reuters and DPA, as well as several years of official SEC filings. This targeted training enables the model to excel at complex downstream financial analysis and domain-specific reasoning.

Breakup of datasets used for finetuning

Dataset	Documents	Mean Words	Mean Tokens	Time Period
Reuters news	14,574,641	369.23	459.09	1st Jan 2003-31st Dec 2012
DPA news	387,187	286.20	390.37	1st Jun 2001-31st May 2011
SEC filings	12,238,570	379.96	536.56	31st Mar 2009-31st Oct 2023

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/FinBloom_7B"

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}, 
}