FinGPT

FinGPT: Open-Source Financial Large Language Models

Let us not expect Wall Street to open-source LLMs or open APIs, due to FinTech institutes' internal regulations and policies.

Blueprint of FinGPT

https://huggingface.co/FinGPT

Project Contributors

FinGPT is an open-source financial large language model project developed and maintained by the AI4Finance Foundation.

Key contributors include:

• Hongyang (Bruce) Yang – research and development on financial large language models and related applications
• [other contributors…]

What's New:

• [Model Release] Nov, 2023: We release FinGPT-Forecaster! 🔥Demo, Medium Blog & Model are available on Huggingface🤗!
• [Paper Acceptance] Oct, 2023: "FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets" is accepted🎉 by Instruction Workshop @ NeurIPS 2023
• [Paper Acceptance] Oct, 2023: "FinGPT: Democratizing Internet-scale Data for Financial Large Language Models" is accepted🎉 by Instruction Workshop @ NeurIPS 2023
• [Model Release] Oct, 2023: We release the financial multi-task LLMs 🔥 produced when evaluating base-LLMs on FinGPT-Benchmark
• [Paper Acceptance] Sep, 2023: "Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models" is accepted🎉 by ACM International Conference on AI in Finance (ICAIF-23)
• [Model Release] Aug, 2023: We release the financial sentiment analysis model 🔥
• [Paper Acceptance] Jul, 2023: "Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models" is accepted🎉 by FinLLM 2023@IJCAI 2023
• [Paper Acceptance] Jul, 2023: "FinGPT: Open-Source Financial Large Language Models" is accepted🎉 by FinLLM 2023@IJCAI 2023
• [Medium Blog] Jun 2023: FinGPT: Powering the Future of Finance with 20 Cutting-Edge Applications

Quick Start

🚀 Get started with FinGPT in minutes!

For detailed setup instructions for running FinGPT locally or on Replit, including hardware requirements and troubleshooting, check out our comprehensive SETUP Guide.

❓ Have questions? Check our FAQ for answers to common questions about FinGPT usage, capabilities, and limitations.

Installation

# Clone the repository
git clone https://github.com/AI4Finance-Foundation/FinGPT.git
cd FinGPT

# Install dependencies
pip install -r requirements.txt
pip install -e .

Try the Demo

Visit our HuggingFace Space to try FinGPT-Forecaster without any installation!

Basic Usage

# Using cloud API (no GPU required)
import os
os.environ['FINGPT_LLM_PROVIDER'] = 'openai'

# Using local models (requires GPU)
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# See SETUP.md for detailed examples

Why FinGPT?

1). Finance is highly dynamic. BloombergGPT trained an LLM using a mixture of finance data and general-purpose data, which took about 53 days, at a cost of around $3M). It is costly to retrain an LLM model like BloombergGPT every month or every week, thus lightweight adaptation is highly favorable. FinGPT can be fine-tuned swiftly to incorporate new data (the cost falls significantly, less than $300 per fine-tuning).

2). Democratizing Internet-scale financial data is critical, say allowing timely updates of the model (monthly or weekly updates) using an automatic data curation pipeline. BloombergGPT has privileged data access and APIs, while FinGPT presents a more accessible alternative. It prioritizes lightweight adaptation, leveraging the best available open-source LLMs.

3). The key technology is "RLHF (Reinforcement learning from human feedback)", which is missing in BloombergGPT. RLHF enables an LLM model to learn individual preferences (risk-aversion level, investing habits, personalized robo-advisor, etc.), which is the "secret" ingredient of ChatGPT and GPT4.

Milestone of AI Robo-Advisor: FinGPT-Forecaster

Try the latest released FinGPT-Forecaster demo at our HuggingFace Space

The dataset for FinGPT-Forecaster: https://huggingface.co/datasets/FinGPT/fingpt-forecaster-dow30-202305-202405

Market Support: FinGPT-Forecaster is currently trained on US market data (DOW 30). For other markets (including China stock market), you would need to:

1. Collect appropriate market data
2. Fine-tune the model on the target market data
3. Consider using FinGPT v1.x models which were designed for Chinese markets
4. Adapt the data pipeline and model architecture for specific market characteristics

Enter the following inputs:

1. ticker symbol (e.g. AAPL, MSFT, NVDA)
2. the day from which you want the prediction to happen (yyyy-mm-dd)
3. the number of past weeks where market news are retrieved
4. whether to add the latest basic financials as additional information

Click Submit！ And you'll be responded with a well-rounded analysis of the company and a prediction for next week's stock price movement!

For detailed and more customized implementation, please refer to FinGPT-Forecaster

FinGPT Demos:

Current State-of-the-arts for Financial Sentiment Analysis

• FinGPT V3 (Updated on 10/12/2023)

  • What's new: Best trainable and inferable FinGPT for sentiment analysis on a single RTX 3090, which is even better than GPT-4 and ChatGPT Finetuning.

  • FinGPT v3 series are LLMs finetuned with the LoRA method on the News and Tweets sentiment analysis dataset which achieve the best scores on most of the financial sentiment analysis datasets with low cost.

  • FinGPT v3.3 use llama2-13b as base model; FinGPT v3.2 uses llama2-7b as base model; FinGPT v3.1 uses chatglm2-6B as base model.

  • Benchmark Results:

  • Weighted F1	FPB	FiQA-SA	TFNS	NWGI	Devices	Time	Cost
    FinGPT v3.3	0.882	0.874	0.903	0.643	1 × RTX 3090	17.25 hours	$17.25
    FinGPT v3.2	0.850	0.860	0.894	0.636	1 × A100	5.5 hours	$ 22.55
    FinGPT v3.1	0.855	0.850	0.875	0.642	1 × A100	5.5 hours	$ 22.55
    FinGPT (8bit)	0.855	0.847	0.879	0.632	1 × RTX 3090	6.47 hours	$ 6.47
    FinGPT (QLoRA)	0.777	0.752	0.828	0.583	1 × RTX 3090	4.15 hours	$ 4.15
    OpenAI Fine-tune	0.878	0.887	0.883	-	-	-	-
    GPT-4	0.833	0.630	0.808	-	-	-	-
    FinBERT	0.880	0.596	0.733	0.538	4 × NVIDIA K80 GPU	-	-
    Llama2-7B	0.390	0.800	0.296	0.503	2048 × A100	21 days	$ 4.23 million
    BloombergGPT	0.511	0.751	-	-	512 × A100	53 days	$ 2.67 million

    Cost per GPU hour. For A100 GPUs, the AWS p4d.24xlarge instance, equipped with 8 A100 GPUs is used as a benchmark to estimate the costs. Note that BloombergGPT also used p4d.24xlarge As of July 11, 2023, the hourly rate for this instance stands at $32.773. Consequently, the estimated cost per GPU hour comes to $32.77 divided by 8, resulting in approximately $4.10. With this value as the reference unit price (1 GPU hour). BloombergGPT estimated cost= 512 x 53 x 24 = 651,264 GPU hours x $4.10 = $2,670,182.40. For RTX 3090, we assume its cost per hour is approximately $1.0, which is actually much higher than available GPUs from platforms like vast.ai.

  • Reproduce the results by running benchmarks, and the detailed tutorial is on the way.

  • Finetune your own FinGPT v3 model with the LoRA method on only an RTX 3090 with this notebook in 8bit or this notebook in int4 (QLoRA)

• FinGPT V1

  • FinGPT by finetuning ChatGLM2 / Llama2 with LoRA with the market-labeled data for the Chinese Market

Instruction Tuning Datasets and Models

The datasets we used, and the multi-task financial LLM models are available at https://huggingface.co/FinGPT

Our Code

Datasets	Train Rows	Test Rows	Description
fingpt-sentiment-train	76.8K	N/A	Sentiment Analysis Training Instructions
fingpt-finred	27.6k	5.11k	Financial Relation Extraction Instructions
fingpt-headline	82.2k	20.5k	Financial Headline Analysis Instructions
fingpt-ner	511	98	Financial Named-Entity Recognition Instructions
fingpt-fiqa_qa	17.1k	N/A	Financial Q&A Instructions
fingpt-fineval	1.06k	265	Chinese Multiple-Choice Questions Instructions

Multi-task financial LLMs Models:

  demo_tasks = [
      'Financial Sentiment Analysis',
      'Financial Relation Extraction',
      'Financial Headline Classification',
      'Financial Named Entity Recognition',]
  demo_inputs = [
      "Glaxo's ViiV Healthcare Signs China Manufacturing Deal With Desano",
      "Apple Inc. Chief Executive Steve Jobs sought to soothe investor concerns about his health on Monday, saying his weight loss was caused by a hormone imbalance that is relatively simple to treat.",
      'gold trades in red in early trade; eyes near-term range at rs 28,300-28,600',
      'This LOAN AND SECURITY AGREEMENT dated January 27 , 1999 , between SILICON VALLEY BANK (" Bank "), a California - chartered bank with its principal place of business at 3003 Tasman Drive , Santa Clara , California 95054 with a loan production office located at 40 William St ., Ste .',]
  demo_instructions = [
      'What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}.',
      'Given phrases that describe the relationship between two words/phrases as options, extract the word/phrase pair and the corresponding lexical relationship between them from the input text. The output format should be "relation1: word1, word2; relation2: word3, word4". Options: product/material produced, manufacturer, distributed by, industry, position held, original broadcaster, owned by, founded by, distribution format, headquarters location, stock exchange, currency, parent organization, chief executive officer, director/manager, owner of, operator, member of, employer, chairperson, platform, subsidiary, legal form, publisher, developer, brand, business division, location of formation, creator.',
      'Does the news headline talk about price going up? Please choose an answer from {Yes/No}.',
      'Please extract entities and their types from the input sentence, entity types should be chosen from {person/organization/location}.',]

Models	Description	Function
fingpt-mt_llama2-7b_lora	Fine-tuned Llama2-7b model with LoRA	Multi-Task
fingpt-mt_falcon-7b_lora	Fine-tuned falcon-7b model with LoRA	Multi-Task
fingpt-mt_bloom-7b1_lora	Fine-tuned bloom-7b1 model with LoRA	Multi-Task
fingpt-mt_mpt-7b_lora	Fine-tuned mpt-7b model with LoRA	Multi-Task
fingpt-mt_chatglm2-6b_lora	Fine-tuned chatglm-6b model with LoRA	Multi-Task
fingpt-mt_qwen-7b_lora	Fine-tuned qwen-7b model with LoRA	Multi-Task
fingpt-sentiment_llama2-13b_lora	Fine-tuned llama2-13b model with LoRA	Single-Task
fingpt-forecaster_dow30_llama2-7b_lora	Fine-tuned llama2-7b model with LoRA	Single-Task

Tutorials

[Training] Beginner’s Guide to FinGPT: Training with LoRA and ChatGLM2–6B One Notebook, $10 GPU

Understanding FinGPT: An Educational Blog Series

• FinGPT: Powering the Future of Finance with 20 Cutting-Edge Applications
• FinGPT I: Why We Built the First Open-Source Large Language Model for Finance
• FinGPT II: Cracking the Financial Sentiment Analysis Task Using Instruction Tuning of General-Purpose Large Language Models

FinGPT Ecosystem

FinGPT embraces a full-stack framework for FinLLMs with five layers:

1. Data source layer: This layer assures comprehensive market coverage, addressing the temporal sensitivity of financial data through real-time information capture.
2. Data engineering layer: Primed for real-time NLP data processing, this layer tackles the inherent challenges of high temporal sensitivity and low signal-to-noise ratio in financial data.
3. LLMs layer: Focusing on a range of fine-tuning methodologies such as LoRA, this layer mitigates the highly dynamic nature of financial data, ensuring the model’s relevance and accuracy.
4. Task layer: This layer is responsible for executing fundamental tasks. These tasks serve as the benchmarks for performance evaluations and cross-comparisons in the realm of FinLLMs
5. Application layer: Showcasing practical applications and demos, this layer highlights the potential capability of FinGPT in the financial sector.

• FinGPT Framework: Open-Source Financial Large Language Models

• FinGPT-RAG: We present a retrieval-augmented large language model framework specifically designed for financial sentiment analysis, optimizing information depth and context through external knowledge retrieval, thereby ensuring nuanced predictions.

• FinGPT-FinNLP: FinNLP provides a playground for all people interested in LLMs and NLP in Finance. Here we provide full pipelines for LLM training and finetuning in the field of finance. The full architecture is shown in the following picture. Detail codes and introductions can be found here. Or you may refer to the wiki

• FinGPT-Benchmark: We introduce a novel Instruction Tuning paradigm optimized for open-source Large Language Models (LLMs) in finance, enhancing their adaptability to diverse financial datasets while also facilitating cost-effective, systematic benchmarking from task-specific, multi-task, and zero-shot instruction tuning tasks.

Cloud LLM Providers for FinGPT Inference

For the Finogrid platform (finogrid/), FinGPT supports multiple cloud LLM providers as alternatives to running local models. Set FINGPT_LLM_PROVIDER in your environment:

Provider	Model	Context Length	Use Case
OpenAI	GPT-3.5-turbo	16K	Default fallback for sentiment & agents
MiniMax	MiniMax-M3	512K	Latest flagship model with 128K max output and image input support
FinGPT (local)	Llama-2-13B LoRA	4K	Full local inference (requires GPU)

Open-Source Base Model used in the LLMs layer of FinGPT

• Feel free to contribute more open-source base models tailored for various language-specific financial markets.

Base Model	Pretraining Tokens	Context Length	Model Advantages	Model Size	Experiment Results	Applications
Llama-2	2 Trillion	4096	Llama-2 excels on English-based market data	llama-2-7b and Llama-2-13b	llama-2 consistently shows superior fine-tuning results	Financial Sentiment Analysis, Robo-Advisor
Falcon	1,500B	2048	Maintains high-quality results while being more resource-efficient	falcon-7b	Good for English market data	Financial Sentiment Analysis
MPT	1T	2048	MPT models can be trained with high throughput efficiency and stable convergence	mpt-7b	Good for English market data	Financial Sentiment Analysis
Bloom	366B	2048	World’s largest open multilingual language model	bloom-7b1	Good for English market data	Financial Sentiment Analysis
ChatGLM2	1.4T	32K	Exceptional capability for Chinese language expression	chatglm2-6b	Shows prowess for Chinese market data	Financial Sentiment Analysis, Financial Report Summary
Qwen	2.2T	8k	Fast response and high accuracy	qwen-7b	Effective for Chinese market data	Financial Sentiment Analysis
InternLM	1.8T	8k	Can flexibly and independently construct workflows	internlm-7b	Effective for Chinese market data	Financial Sentiment Analysis

• Benchmark Results for the above open-source Base Models in the financial sentiment analysis task using the same instruction template for SFT (LoRA):

  Weighted F1/Acc	Llama2	Falcon	MPT	Bloom	ChatGLM2	Qwen	InternLM
  FPB	0.863/0.863	0.846/0.849	0.872/0.872	0.810/0.810	0.850/0.849	0.854/0.854	0.709/0.714
  FiQA-SA	0.871/0.855	0.840/0.811	0.863/0.844	0.771/0.753	0.864/0.862	0.867/0.851	0.679/0.687
  TFNS	0.896/0.895	0.893/0.893	0.907/0.907	0.840/0.840	0.859/0.858	0.883/0.882	0.729/0.731
  NWGI	0.649/0.651	0.636/0.638	0.640/0.641	0.573/0.574	0.619/0.629	0.638/0.643	0.498/0.503

All Thanks To Our Contributors :

News

• Columbia Perspectives on ChatGPT
• [MIT Technology Review] ChatGPT is about to revolutionize the economy. We need to decide what that looks like
• [BloombergGPT] BloombergGPT: A Large Language Model for Finance
• [Finextra] ChatGPT and Bing AI to sit as panellists at fintech conference

ChatGPT at AI4Finance

• Hey, ChatGPT! Explain FinRL code to me!

Introductory

• Sparks of artificial general intelligence: Early experiments with GPT-4
• [GPT-4] GPT-4 Technical Report
• [InstructGPT] Training language models to follow instructions with human feedback NeurIPS 2022.

The Journey of Open AI GPT models. GPT models explained. Open AI's GPT-1, GPT-2, GPT-3.

• [GPT-3] Language models are few-shot learners NeurIPS 2020.
• [GPT-2] Language Models are Unsupervised Multitask Learners
• [GPT-1] Improving Language Understanding by Generative Pre-Training
• [Transformer] Attention is All you Need NeurIPS 2017.

(Financial) Big Data

• [BloombergGPT] BloombergGPT: A Large Language Model for Finance

• WHAT’S IN MY AI? A Comprehensive Analysis of Datasets Used to Train GPT-1, GPT-2, GPT-3, GPT-NeoX-20B, Megatron-11B, MT-NLG, and Gopher

• FinRL-Meta Repo and paper FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning. Advances in Neural Information Processing Systems, 2022.

• [AI4Finance] FinNLP Democratizing Internet-scale financial data.

Interesting Demos

• GPT-3 Creative Fiction Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

ChatGPT for FinTech

Citing FinGPT

@article{yang2023fingpt_open,
     title   = {FinGPT: Open-Source Financial Large Language Models},
     author  = {Yang, Hongyang and Liu, Xiao-Yang and Wang, Christina Dan},
     journal = {arXiv preprint arXiv:2306.06031},
     year    = {2023},
     url     = {https://arxiv.org/abs/2306.06031},
     note    = {First official FinGPT paper; FinLLM Workshop at IJCAI 2023}
}

@article{zhang2023instructfingpt,
     title={Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models}, 
     author={Boyu Zhang and Hongyang Yang and Xiao-Yang Liu},
     journal={FinLLM Symposium at IJCAI 2023},
     year={2023}
}

@article{zhang2023fingptrag,
     title={Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models},
     author={Zhang, Boyu and Yang, Hongyang and Zhou, tianyu and Babar, Ali and Liu, Xiao-Yang},
     journal = {ACM International Conference on AI in Finance (ICAIF)},
     year={2023}
}

@article{liang2024fingpt,
     title={FinGPT: enhancing sentiment-based stock movement prediction with dissemination-aware and context-enriched LLMs},
     author={Liang, Yixuan and Liu, Yuncong and Wang, Neng and Yang, Hongyang and Zhang, Boyu and Wang, Christina Dan},
     journal={AAAI 2025 Workshop GoodData},
     year={2025}
}

@article{wang2023fingptbenchmark,
     title={FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets},
     author={Wang, Neng and Yang, Hongyang and Wang, Christina Dan},
     journal={NeurIPS Workshop on Instruction Tuning and Instruction Following},
     year={2023}
}

@article{2023finnlp,
     title={Data-centric FinGPT: Democratizing Internet-scale Data for Financial Large Language Models},
     author={Liu, Xiao-Yang and Wang, Guoxuan and Yang, Hongyang and Zha, Daochen},
     journal={NeurIPS Workshop on Instruction Tuning and Instruction Following},
     year={2023}
}

LICENSE

MIT License

Disclaimer: We are sharing codes for academic purposes under the MIT education license. Nothing herein is financial advice, and NOT a recommendation to trade real money. Please use common sense and always first consult a professional before trading or investing.