Chaitanya14/Financial_Agent_flant5

The Financial Agent flan-t5 is a compact, high-efficiency version of the Financial Agent architecture. Built upon the flan-t5-large backbone, this model has been specifically fine-tuned on the Financial Context Dataset to act as a linguistic bridge between user queries and structured data systems. It specializes in decomposing complex, natural language financial prompts into precise retrieval parameters, enabling a seamless handoff to downstream Data Modules for accurate and optimized information sourcing.

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 AutoModelForSeq2SeqLM, AutoTokenizer

peft_model_id = "Chaitanya14/Financial_Agent_flant5"

config = PeftConfig.from_pretrained(peft_model_id)

tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)
model = PeftModel.from_pretrained(model, peft_model_id)

Citation

If you use the Financial Agent flan-t5 model, please cite with the following BibTex entry:

@article{SINHA2026115559,
title = {FinBloom: Knowledge-Grounding Large Language Model with Real-Time Financial Data},
journal = {Knowledge-Based Systems},
volume = {339},
pages = {115559},
year = {2026},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2026.115559},
url = {https://www.sciencedirect.com/science/article/pii/S0950705126003011},
author = {Ankur Sinha and Chaitanya Agarwal and Pekka Malo},
keywords = {Financial large language model, Generative pre-Trained transformer, Knowledge grounding, Natural language processing},
abstract = {Large language models (LLMs) excel at generating human-like responses but often struggle with interactive tasks that require access to real-time information. This limitation poses challenges in finance, where models must access up-to-date information, such as recent news or price movements, to support decision-making. To address this, we introduce Financial Agent, a knowledge-grounding approach for LLMs to handle financial queries using real-time text and tabular data. Our contributions are threefold: First, we develop a Financial Context Dataset of over 50,000 financial queries paired with the required context. Second, we develop FinBloom 7B, a custom 7 billion parameter LLM, by fine-tuning Bloom 7B on 14 million financial news articles from Reuters and Deutsche Presse-Agentur (DPA), alongside a random sample of 25% from 12 million Securities and Exchange Commission (SEC) filings. Third, we fine-tune FinBloom 7B using the Financial Context Dataset to serve as a Financial Agent. This agent generates relevant financial context, enabling efficient real-time data retrieval to answer user queries. By reducing latency and eliminating the need for users to manually provide accurate data, our approach significantly enhances the capability of LLMs to handle dynamic financial tasks. Our proposed approach makes real-time financial decisions, algorithmic trading and other related tasks streamlined, and is valuable in contexts with high-velocity data flows.}
}