#1111th of 12 in Training and fine-tuning
fingpt
LoRA fine-tuned financial LLMs, datasets and notebooks for sentiment and forecasting
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- Stars
- 21.4k
- License
- MIT
- Last commit
- Sep 2026
- Last release
- Apr 2026
- Language
- Jupyter Notebook
Missed the latest GitHub check; these facts may be older.
Overview
FinGPT is a collection of LoRA adapters, instruction datasets and training notebooks for financial NLP tasks such as sentiment analysis, relation extraction, headline classification, NER and stock-movement forecasting. Adapters are published on Hugging Face for Llama2, Falcon, BLOOM, MPT, ChatGLM2 and Qwen bases. The README reports a Llama2-13B sentiment adapter trained on one RTX 3090 in about 17 hours.
Who it is for: Researchers and engineers fine-tuning LLMs on financial text
Strengths
- Published LoRA adapters and datasets on Hugging Face for several base models
- Notebooks fine-tune on a single RTX 3090 in 8-bit or int4 QLoRA
- Benchmark table reports weighted F1 on FPB, FiQA-SA, TFNS and NWGI with cost per run
- Forecaster has a hosted Hugging Face Space demo, so no install is needed to try it
Weaknesses
- Forecaster is trained on US DOW 30 data only; other markets need your own data and fine-tuning
- Most releases date from 2023 and use older bases such as Llama2 and ChatGLM2
- Research and notebook code, not a packaged app or service; no Docker setup
- Cost comparisons with BloombergGPT rely on the project's own GPU-hour assumptions
What it needs
- GPU optional
- Needs Hugging Face Transformers, PEFT, OpenAI API (optional cloud provider)
- Models: Llama2-7B, Llama2-13B, Falcon-7B, BLOOM-7B1, MPT-7B
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