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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

• [Model Release] Nov, 2023: We releaseFinGPT-Forecaster! 🔥Demo,Medium Blog &Model are available on Huggingface🤗!
• [Paper Acceptance] Oct, 2023:"FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets" is accepted🎉 byInstruction Workshop @ NeurIPS 2023
• [Paper Acceptance] Oct, 2023:"FinGPT: Democratizing Internet-scale Data for Financial Large Language Models" is accepted🎉 byInstruction Workshop @ NeurIPS 2023
• [Model Release] Oct, 2023: We release thefinancial multi-task LLMs 🔥 produced when evaluating base-LLMs onFinGPT-Benchmark
• [Paper Acceptance] Sep, 2023:"Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models" is accepted🎉 byACM International Conference on AI in Finance (ICAIF-23)
• [Model Release] Aug, 2023: We release thefinancial sentiment analysis model 🔥
• [Paper Acceptance] Jul, 2023:"Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models" is accepted🎉 byFinLLM 2023@IJCAI 2023
• [Paper Acceptance] Jul, 2023:"FinGPT: Open-Source Financial Large Language Models" is accepted🎉 byFinLLM 2023@IJCAI 2023
• [Medium Blog] Jun 2023:FinGPT: Powering the Future of Finance with 20 Cutting-Edge Applications

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.

Try the latest released FinGPT-Forecaster demo at ourHuggingFace Space

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 toFinGPT-Forecaster

• FinGPT V3 (Updated on 10/12/2023)

  • What's new:Best trainable and inferable FinGPT for sentiment analysis on a single RTX 3090, which is even better than GPT-4 and ChatGPT Finetuning.

  • FinGPT v3 series are LLMs finetuned with the LoRA method on the News and Tweets sentiment analysis dataset which achieve the best scores on most of the financial sentiment analysis datasets with low cost.

  • FinGPT v3.3 use llama2-13b as base model; FinGPT v3.2 uses llama2-7b as base model; FinGPT v3.1 uses chatglm2-6B as base model.

  • Benchmark Results:

  • Weighted F1	FPB	FiQA-SA	TFNS	NWGI	Devices	Time	Cost
    FinGPT v3.3	0.882	0.874	0.903	0.643	1 × RTX 3090	17.25 hours	$17.25
    FinGPT v3.2	0.850	0.860	0.894	0.636	1 × A100	5.5 hours	$ 22.55
    FinGPT v3.1	0.855	0.850	0.875	0.642	1 × A100	5.5 hours	$ 22.55
    FinGPT (8bit)	0.855	0.847	0.879	0.632	1 × RTX 3090	6.47 hours	$ 6.47
    FinGPT (QLoRA)	0.777	0.752	0.828	0.583	1 × RTX 3090	4.15 hours	$ 4.15
    OpenAI Fine-tune	0.878	0.887	0.883	-	-	-	-
    GPT-4	0.833	0.630	0.808	-	-	-	-
    FinBERT	0.880	0.596	0.733	0.538	4 × NVIDIA K80 GPU	-	-
    Llama2-7B	0.390	0.800	0.296	0.503	2048 × A100	21 days	$ 4.23 million
    BloombergGPT	0.511	0.751	-	-	512 × A100	53 days	$ 2.67 million

    Cost per GPU hour. ForA100 GPUs, the AWS p4d.24xlarge instance, equipped with 8 A100 GPUs is used as a benchmark to estimate the costs. Note that BloombergGPT also used p4d.24xlarge As of July 11, 2023, the hourly rate for this instance stands at $32.773. Consequently, the estimated cost per GPU hour comes to $32.77 divided by 8, resulting in approximately$4.10. With this value as the reference unit price (1 GPU hour).BloombergGPT estimated cost= 512 x 53 x 24 = 651,264 GPU hours x $4.10 = $2,670,182.40. ForRTX 3090, we assume its cost per hour is approximately$1.0, which is actually much higher than available GPUs from platforms like vast.ai.

  • Reproduce the results by runningbenchmarks, and the detailed tutorial is on the way.

  • Finetune your own FinGPT v3 model with the LoRA method on only an RTX 3090 with thisnotebook in 8bit or thisnotebook in int4 (QLoRA)

• FinGPT V1

  • FinGPT by finetuning ChatGLM2 / Llama2 with LoRA with the market-labeled data for the Chinese Market

The datasets we used, and themulti-task financial LLM models are available athttps://huggingface.co/FinGPT

Our Code

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}.',]

[Training] Beginner’s Guide to FinGPT: Training with LoRA and ChatGLM2–6B One Notebook, $10 GPU

• FinGPT: Powering the Future of Finance with 20 Cutting-Edge Applications
• FinGPT I: Why We Built the First Open-Source Large Language Model for Finance
• FinGPT II: Cracking the Financial Sentiment Analysis Task Using Instruction Tuning of General-Purpose Large Language Models

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.

• FinGPT Framework: Open-Source Financial Large Language Models

• FinGPT-RAG: We present a retrieval-augmented large language model framework specifically designed for financial sentiment analysis, optimizing information depth and context through external knowledge retrieval, thereby ensuring nuanced predictions.

• FinGPT-FinNLP: FinNLP provides a playground for all people interested in LLMs and NLP in Finance. Here we provide full pipelines for LLM training and finetuning in the field of finance. The full architecture is shown in the following picture. Detail codes and introductions can be foundhere. Or you may refer to thewiki

• FinGPT-Benchmark: We introduce a novel Instruction Tuning paradigm optimized for open-source Large Language Models (LLMs) in finance, enhancing their adaptability to diverse financial datasets while also facilitating cost-effective, systematic benchmarking from task-specific, multi-task, and zero-shot instruction tuning tasks.

• Feel free to contribute more open-source base models tailored for various language-specific financial markets.

• Benchmark Results for the above open-source Base Models in the financial sentiment analysis task using the same instruction template for SFT (LoRA):

  Weighted F1/Acc	Llama2	Falcon	MPT	Bloom	ChatGLM2	Qwen	InternLM
  FPB	0.863/0.863	0.846/0.849	0.872/0.872	0.810/0.810	0.850/0.849	0.854/0.854	0.709/0.714
  FiQA-SA	0.871/0.855	0.840/0.811	0.863/0.844	0.771/0.753	0.864/0.862	0.867/0.851	0.679/0.687
  TFNS	0.896/0.895	0.893/0.893	0.907/0.907	0.840/0.840	0.859/0.858	0.883/0.882	0.729/0.731
  NWGI	0.649/0.651	0.636/0.638	0.640/0.641	0.573/0.574	0.619/0.629	0.638/0.643	0.498/0.503

• Columbia Perspectives on ChatGPT
• [MIT Technology Review]ChatGPT is about to revolutionize the economy. We need to decide what that looks like
• [BloombergGPT]BloombergGPT: A Large Language Model for Finance
• [Finextra]ChatGPT and Bing AI to sit as panellists at fintech conference

• [YouTube video]I Built a Trading Bot with ChatGPT, combining ChatGPT and FinRL.
• Hey, ChatGPT! Explain FinRL code to me!

• Sparks of artificial general intelligence: Early experiments with GPT-4
• [GPT-4]GPT-4 Technical Report
• [InstructGPT]Training language models to follow instructions with human feedback NeurIPS 2022.

The Journey of Open AI GPT models. GPT models explained. Open AI's GPT-1, GPT-2, GPT-3.

• [GPT-3]Language models are few-shot learners NeurIPS 2020.
• [GPT-2]Language Models are Unsupervised Multitask Learners
• [GPT-1]Improving Language Understanding by Generative Pre-Training
• [Transformer]Attention is All you Need NeurIPS 2017.

• [BloombergGPT]BloombergGPT: A Large Language Model for Finance

• WHAT’S IN MY AI? A Comprehensive Analysis of Datasets Used to Train GPT-1, GPT-2, GPT-3, GPT-NeoX-20B, Megatron-11B, MT-NLG, and Gopher

• FinRL-Meta Repo and paperFinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning. Advances in Neural Information Processing Systems, 2022.

• [AI4Finance]FinNLP Democratizing Internet-scale financial data.

• GPT-3 Creative Fiction Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

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@article{yang2023fingpt,  title={FinGPT: Open-Source Financial Large Language Models},  author={Yang, Hongyang and Liu, Xiao-Yang and Wang, Christina Dan},  journal={FinLLM Symposium at IJCAI 2023},  year={2023}}@article{zhang2023instructfingpt,      title={Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models},       author={Boyu Zhang and Hongyang Yang and Xiao-Yang Liu},      journal={FinLLM Symposium at IJCAI 2023},      year={2023}}@article{zhang2023fingptrag,  title={Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models},  author={Zhang, Boyu and Yang, Hongyang and Zhou, tianyu and Babar, Ali and Liu, Xiao-Yang}, journal = {ACM International Conference on AI in Finance (ICAIF)},  year={2023}}@article{wang2023fingptbenchmark,  title={FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets},  author={Wang, Neng and Yang, Hongyang and Wang, Christina Dan},  journal={NeurIPS Workshop on Instruction Tuning and Instruction Following},  year={2023}}@article{2023finnlp,  title={Data-centric FinGPT: Democratizing Internet-scale Data for Financial Large Language Models},  author={Liu, Xiao-Yang and Wang, Guoxuan and Yang, Hongyang and Zha, Daochen},  journal={NeurIPS Workshop on Instruction Tuning and Instruction Following},  year={2023}}

MIT License

Disclaimer: We are sharing codes for academic purposes under the MIT education license. Nothing herein is financial advice, and NOT a recommendation to trade real money. Please use common sense and always first consult a professional before trading or investing.