Overview
FinGPT is an open-source ecosystem of financial large language models that provides data pipelines, instruction-tuning datasets, the FinGPT-Benchmark, and a retrieval-augmented (RAG) framework. By leveraging lightweight fine-tuning methods such as LoRA and QLoRA and curated financial task suites, FinGPT lowers the barrier to train and deploy finance-specific models on limited compute while providing reproducible teaching and research materials.
Key features
- Multi-task financial instruction datasets and benchmarks covering sentiment analysis, relation extraction, NER, and QA.
- Support for low-cost fine-tuning methods (LoRA/QLoRA) to balance performance and compute.
- FinGPT-RAG: retrieval-augmented framework tailored for financial tasks to improve timeliness and factuality.
Use cases
- Financial sentiment analysis and media monitoring for news, filings and social feeds.
- Financial QA and report summarization to assist research and automate reporting workflows.
- Teaching and research: course labs, reproducible experiments, and benchmarking.
Technical highlights
- Instruction tuning on domain-specific datasets and model adaptation using LoRA/QLoRA.
- Integration of RAG pipelines and domain data engineering to ensure evidence-driven outputs.
- Comprehensive notebooks, scripts and CI to reproduce experiments locally or on cloud infrastructure.