# FinGPT — Open-Source Financial Large Language Models

> FinGPT is an open-source framework for financial large language models. It provides data pipelines, fine-tuning recipes, and evaluation benchmarks for building AI applications in finance, including sentiment analysis, stock prediction, and robo-advising.

## Install

Save in your project root:

# FinGPT — Open-Source Financial Large Language Models

## Quick Use
```bash
git clone https://github.com/AI4Finance-Foundation/FinGPT.git
cd FinGPT
pip install -r requirements.txt
# Follow the fine-tuning tutorials in the fingpt/ directory
```

## Introduction
FinGPT is an open-source project from the AI4Finance Foundation that democratizes financial AI research. It provides end-to-end pipelines for collecting financial data, fine-tuning large language models on financial tasks, and evaluating them against domain-specific benchmarks, lowering the barrier to building AI-powered financial applications.

## What FinGPT Does
- Aggregates financial data from news APIs, SEC filings, social media, and market feeds
- Fine-tunes open LLMs (LLaMA, ChatGLM, Falcon) on financial NLP tasks using LoRA and QLoRA
- Performs financial sentiment analysis on earnings calls, news headlines, and social posts
- Supports stock movement prediction, risk assessment, and financial report summarization
- Provides reproducible training scripts and Jupyter notebooks for each financial task

## Architecture Overview
FinGPT follows a three-layer design: a data layer that handles real-time and historical financial data ingestion, a model layer that applies parameter-efficient fine-tuning (PEFT) methods like LoRA to base LLMs, and an application layer with task-specific evaluation and deployment tools. The framework uses Hugging Face Transformers and PEFT libraries under the hood.

## Self-Hosting & Configuration
- Clone the repository and install dependencies with pip
- Download base models from Hugging Face (LLaMA 2, Mistral, or ChatGLM)
- Configure data sources by setting API keys for financial data providers
- Run fine-tuning scripts with configurable LoRA rank, learning rate, and batch size
- Deploy fine-tuned models locally or via Hugging Face Inference Endpoints

## Key Features
- Multiple financial NLP tasks covered: sentiment, NER, summarization, and prediction
- Parameter-efficient fine-tuning keeps GPU requirements manageable (single A100 or 4090)
- Robo-advisor demo that combines market data with LLM reasoning
- Reinforcement learning from AI feedback (RLAIF) pipeline for financial alignment
- Active research community with regular paper publications and model releases

## Comparison with Similar Tools
- **BloombergGPT** — proprietary model trained on Bloomberg data; FinGPT is fully open source
- **FinBERT** — BERT-based sentiment model; FinGPT uses larger generative models for broader tasks
- **ChatGPT/GPT-4** — general-purpose; FinGPT is domain-tuned for financial accuracy
- **Alpaca Finance** — DeFi protocol; FinGPT focuses on NLP and analytics, not on-chain trading
- **PandasAI** — data querying tool; FinGPT handles model training and financial reasoning

## FAQ
**Q: Do I need financial data subscriptions?**
A: Some data sources are free (Yahoo Finance, Reddit). Premium sources like Bloomberg require separate subscriptions.

**Q: What GPU do I need for fine-tuning?**
A: A single GPU with 24 GB VRAM (e.g., RTX 4090 or A100) is sufficient when using QLoRA.

**Q: Can I use FinGPT for live trading?**
A: FinGPT provides analysis and prediction tools, not a trading execution engine. Integrate its outputs with a separate trading platform.

**Q: Which base models work best?**
A: Results vary by task. LLaMA 2 and Mistral models generally perform well for English-language financial tasks.

## Sources
- https://github.com/AI4Finance-Foundation/FinGPT
- https://ai4finance.org/

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Source: https://tokrepo.com/en/workflows/asset-beb37c8d
Author: AI Open Source