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FIN-GPT.AI

FinGPT: Open-source financial LLMs with real-time data and low-cost fine-tuning

Finance· 4.5·0 saves·Freemium

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FinGPT: Open-source financial LLMs with real-time data and low-cost fine-tuning
Pricing
Freemium
Editor rating
4.5 / 5
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About FIN-GPT.AI

FinGPT is an open-source financial large language model (LLM) platform that democratizes financial AI with zero-cost training, a modular real-time data pipeline from 117+ sources, and efficient fine-tuning methods (LoRA/QLoRA/RLSP). It offers pre-trained FinGPT models (v3.3 for robo-advising, v3.2 for sentiment), the FinGPT-Forecaster (THG 7B/13B) for time series prediction, and reproducible deployment via Docker, Hugging Face, and cloud. Benchmarks show FinGPT surpasses GPT-4 in robo-advising and FinBERT in sentiment analysis, enabling applications in trading, risk, and advisory.

Pros

  • Open-source, MIT-licensed financial LLM platform
  • Zero-cost training paradigm with massive real-time data
  • Modular pipeline spanning 117+ data sources (news, social, filings, markets)
  • Lightweight adaptation via LoRA and QLoRA; RLSP for alignment
  • Pre-trained FinGPT models: v3.3 (robo-advising) and v3.2 (sentiment)
  • FinGPT-Forecaster THG (7B/13B) for time series prediction
  • FinGPT-Bench for finance-specific evaluation (sentiment, NER, RE, QA)
  • Multi-granularity processing at ticker, industry, market, and global levels
  • One-click deployment via Docker, Kubernetes, Hugging Face, and Colab
  • Benchmark-leading results vs. GPT-4 (robo-advising) and FinBERT (sentiment)
  • Live Data Loader, Insights Miner, and Clean Data Curator modules
  • Reproducible, low-cost fine-tuning ($17–$300) on cloud GPUs

Cons

    Pricing

    Open-source (self-hosted)
    $0
    • • All core models, code, datasets, and tools available via GitHub
    • • Permissive licenses (Apache 2.0, MIT) across repos
    • • 72+ financial LLMs and leaderboards
    • • Data pipelines (e.g., Yahoo Finance integration), FinNLP, FinRobot
    • • Demos and Colab notebooks
    • • Evaluation benchmarks and deployment scripts
    • • Customization incl. RLHF and low-resource optimizations
    • • Community support via GitHub/Discord; no paid tiers