FinGPT README

The project's README from github.com/AI4Finance-Foundation/FinGPT. As one markdown file: README.md.

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FinGPT: Open-Source Financial Large Language Models

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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

Visitors Discord

Project Contributors

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

Key contributors include:

What's New:

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.

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

demo_interface

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

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 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.

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
Fin

FinGPT (AI4Finance-Foundation/FinGPT)

financial-analysis
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21.4K ·GitHub
FinGPT
What is this?FinGPT is AI4Finance Foundation's open-source project for financial large language models: trained models are released on HuggingFace, with code for RAG, fine-tuning pipelines and benchmarks built around them.
Is FinGPT one model, or a whole toolbox?A stack of parts rather than a single model: FinGPT-RAG (retrieval-augmented framework for financial sentiment analysis), FinGPT-FinNLP (training and fine-tuning pipelines) and FinGPT-Benchmark (evaluation).
Where do the trained models live, and how do I start using them?Trained models are published on HuggingFace for ready use, or you can go through the FinGPT-FinNLP pipelines that cover full LLM training and fine-tuning for finance; the project wiki documents the details.
What do I need installed before anything runs?A Python environment — the repo is Python and Jupyter Notebook code with no Docker setup, so you run the pipelines and notebooks yourself on your own machine.
Is this a polished product or still a research codebase?It's research-grade code rather than a finished app: expect notebooks, pipelines and benchmarks you set up yourself, no packaged installer, and local training still needs your own compute.
Do I need a Wall Street background to use it?No: the FinNLP part is explicitly a playground for anyone interested in LLMs and NLP in finance — researchers, developers and enthusiasts exploring financial applications of language models.
Do I need my own GPU to run FinGPT?Not necessarily: on the Finogrid platform you can set FINGPTLLMPROVIDER to a cloud provider instead of running local models — OpenAI GPT-3.5-turbo (16K) is the default fallback for sentiment and agents, MiniMax-M3 offers a 512K context.
Why open-source financial LLMs at all?The project starts from the premise that Wall Street won't release open LLMs or open APIs because of FinTech institutes' internal regulations and policies — FinGPT exists to be the open alternative anyone can build on.
How is model quality actually measured?Through FinGPT-Benchmark: a novel instruction-tuning paradigm for open-source LLMs in finance, supporting task-specific, multi-task and zero-shot tuning so benchmarking across diverse financial datasets stays systematic and cost-effective.
How popular is FinGPT?Very: around 21.4k GitHub stars, maintained by the AI4Finance Foundation, with trained models published on HuggingFace for direct download.
What are similar projects?jeeves, airllm, verl, MiniCPM, train-llm-from-scratch, stanford_alpaca, RD-Agent, llm-course
Which topics is it in?financial-analysis, technical-analysis, instruction-tuning, sentiment-analysis, quantitative-finance, fintech, training, finance, reinforcement-learning, jupyter notebook, fine-tuning, chatgpt
Creator
AI4Finance-Foundation
Size
12.9 MB
Stars
21.4K
Updated
CommitSep 23Releasev1.0.0Apr 08
Age
3y
Commits · 90 Days