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FinGPT: Open-Source Financial Large Language Models Let us not expect Wall Street to open-source LLMs or open APIs, due to FinTech institutes' internal regulations and policies. Blueprint of FinGPT [Hidden Content] What's New: [Model Release] Nov, 2023: We release FinGPT-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 by Instruction Workshop @ NeurIPS 2023 [Paper Acceptance] Oct, 2023: "FinGPT: Democratizing Internet-scale Data for Financial Large Language Models" is accepted by Instruction Workshop @ NeurIPS 2023 [Model Release] Oct, 2023: We release the financial multi-task LLMs produced when evaluating base-LLMs on FinGPT-Benchmark [Paper Acceptance] Sep, 2023: "Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models" is accepted by ACM International Conference on AI in Finance (ICAIF-23) [Model Release] Aug, 2023: We release the financial sentiment analysis model [Paper Acceptance] Jul, 2023: "Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models" is accepted by FinLLM 2023@IJCAI 2023 [Paper Acceptance] Jul, 2023: "FinGPT: Open-Source Financial Large Language Models" is accepted by FinLLM 2023@IJCAI 2023 [Medium Blog] Jun 2023: FinGPT: Powering the Future of Finance with 20 Cutting-Edge Applications Why FinGPT? 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. Milestone of AI Robo-Advisor: FinGPT-Forecaster Try the latest released FinGPT-Forecaster demo at our HuggingFace Space The dataset for FinGPT-Forecaster: [Hidden Content] Enter the following inputs: ticker symbol (e.g. AAPL, MSFT, NVDA) the day from which you want the prediction to happen (yyyy-mm-dd) the number of past weeks where market news are retrieved 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 to FinGPT-Forecaster FinGPT Demos: Current State-of-the-arts for Financial Sentiment Analysis 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. For A100 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. For RTX 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 running benchmarks, and the detailed tutorial is on the way. Finetune your own FinGPT v3 model with the LoRA method on only an RTX 3090 with this notebook in 8bit or this notebook in int4 (QLoRA) FinGPT V1 FinGPT by finetuning ChatGLM2 / Llama2 with LoRA with the market-labeled data for the Chinese Market Instruction Tuning Datasets and Models The datasets we used, and the multi-task financial LLM models are available at [Hidden Content] Our Code Datasets Train Rows Test Rows Description fingpt-sentiment-train 76.8K N/A Sentiment Analysis Training Instructions fingpt-finred 27.6k 5.11k Financial Relation Extraction Instructions fingpt-headline 82.2k 20.5k Financial Headline Analysis Instructions fingpt-ner 511 98 Financial Named-Entity Recognition Instructions fingpt-fiqa_qa 17.1k N/A Financial Q&A Instructions fingpt-fineval 1.06k 265 Chinese Multiple-Choice Questions Instructions 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}.',] Models Description Function fingpt-mt_llama2-7b_lora Fine-tuned Llama2-7b model with LoRA Multi-Task fingpt-mt_falcon-7b_lora Fine-tuned falcon-7b model with LoRA Multi-Task fingpt-mt_bloom-7b1_lora Fine-tuned bloom-7b1 model with LoRA Multi-Task fingpt-mt_mpt-7b_lora Fine-tuned mpt-7b model with LoRA Multi-Task fingpt-mt_chatglm2-6b_lora Fine-tuned chatglm-6b model with LoRA Multi-Task fingpt-mt_qwen-7b_lora Fine-tuned qwen-7b model with LoRA Multi-Task fingpt-sentiment_llama2-13b_lora Fine-tuned llama2-13b model with LoRA Single-Task fingpt-forecaster_dow30_llama2-7b_lora Fine-tuned llama2-7b model with LoRA Single-Task Tutorials [Training] Beginner’s Guide to FinGPT: Training with LoRA and ChatGLM2–6B One Notebook, $10 GPU Understanding FinGPT: An Educational Blog Series 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 FinGPT Ecosystem FinGPT embraces a full-stack framework for FinLLMs with five layers: Data source layer: This layer assures comprehensive market coverage, addressing the temporal sensitivity of financial data through real-time information capture. 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. 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. 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 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 found here. Or you may refer to the wiki 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. Open-Source Base Model used in the LLMs layer of FinGPT Feel free to contribute more open-source base models tailored for various language-specific financial markets. Base Model Pretraining Tokens Context Length Model Advantages Model Size Experiment Results Applications Llama-2 2 Trillion 4096 Llama-2 excels on English-based market data llama-2-7b and Llama-2-13b llama-2 consistently shows superior fine-tuning results Financial Sentiment Analysis, Robo-Advisor Falcon 1,500B 2048 Maintains high-quality results while being more resource-efficient falcon-7b Good for English market data Financial Sentiment Analysis MPT 1T 2048 MPT models can be trained with high throughput efficiency and stable convergence mpt-7b Good for English market data Financial Sentiment Analysis Bloom 366B 2048 World’s largest open multilingual language model bloom-7b1 Good for English market data Financial Sentiment Analysis ChatGLM2 1.4T 32K Exceptional capability for Chinese language expression chatglm2-6b Shows prowess for Chinese market data Financial Sentiment Analysis, Financial Report Summary Qwen 2.2T 8k Fast response and high accuracy qwen-7b Effective for Chinese market data Financial Sentiment Analysis InternLM 1.8T 8k Can flexibly and independently construct workflows internlm-7b Effective for Chinese market data Financial Sentiment Analysis 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 News 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 ChatGPT at AI4Finance [YouTube video] I Built a Trading Bot with ChatGPT, combining ChatGPT and FinRL. Hey, ChatGPT! Explain FinRL code to me! Introductory 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. (Financial) Big Data [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 paper FinRL-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. Interesting Demos 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. ChatGPT for FinTech ChatGPT Trading Bot [YouTube video] ChatGPT Trading strategy 20097% returns [YouTube video] ChatGPT Coding - Make A Profitable Trading Strategy In Five Minutes! [YouTube video] Easy Automated Live Trading using ChatGPT (+9660.3% hands free) [YouTube video] ChatGPT Trading Strategy 893% Returns [YouTube video] ChatGPT 10 Million Trading Strategy [YouTube video] ChatGPT: Your Crypto Assistant [YouTube video] Generate Insane Trading Returns with ChatGPT and TradingView Citing FinGPT News 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 ChatGPT at AI4Finance [YouTube video] I Built a Trading Bot with ChatGPT, combining ChatGPT and FinRL. Hey, ChatGPT! Explain FinRL code to me! Introductory 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. (Financial) Big Data [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 paper FinRL-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. Interesting Demos 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. ChatGPT for FinTech ChatGPT Trading Bot [YouTube video] ChatGPT Trading strategy 20097% returns [YouTube video] ChatGPT Coding - Make A Profitable Trading Strategy In Five Minutes! [YouTube video] Easy Automated Live Trading using ChatGPT (+9660.3% hands free) [YouTube video] ChatGPT Trading Strategy 893% Returns [YouTube video] ChatGPT 10 Million Trading Strategy [YouTube video] ChatGPT: Your Crypto Assistant [YouTube video] Generate Insane Trading Returns with ChatGPT and TradingView Citing FinGPT @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} } LICENSE 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. GitHub: [Hidden Content]

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New AI Initiative Meta is working on developing its own AI search engine designed to provide conversational answers about current events, aiming to integrate this functionality with its Meta AI chatbot. Reducing Reliance This move is intended to lessen Meta’s dependence on external search engines like Google and Microsoft Bing, which currently supply information on news, sports, and stocks for the Meta AI platform. Strategic Backup The new search engine will serve as a backup solution for Meta, ensuring they are not reliant on external partners if those companies choose to withdraw their services. Competitive Landscape As Meta competes with companies like OpenAI in the AI space, developing its own search capabilities is crucial for maintaining a competitive edge in providing information and enhancing user experience. Future Implications This initiative could reshape how users interact with information on Meta’s platforms, potentially leading to more personalized and contextually relevant search results. Read more at: The Information | Reuters

This week Google-backed Anthropic announced its upgraded AI model Claude 3.5 Sonnet could "perform tasks like navigating web browsers, filling forms, and manipulating data." Now Google plans something similar for Chrome, reports 9to5Linux.com:According to The Information, Google is "developing artificial intelligence that takes over a person's web browser to complete tasks such as gathering research, purchasing a product or booking a flight." "Project Jarvis" — in a nod to J.A.R.V.I.S. in Iron Man — would operate in Google Chrome and is a consumer-facing (rather than enterprise) feature to "automate everyday, web-based tasks." The article doesn't specify whether this would be for mobile or desktop... Given a command/action, Jarvis works by taking "frequent screenshots of what's on their computer screen, and interpreting the shots before taking actions like clicking on a button or typing into a text field." The Information reports that Google "plans to preview the product, also known as a computer-using agent, as early as December alongside the release of its next flagship Gemini large language model, which would help power the product, two of the people said." Source: 9to5google

Official Announcement OpenAI has stated it will not release the AI model code-named Orion this year, despite previous reports suggesting otherwise. Clarification from Spokesperson A spokesperson clarified to TechCrunch that there are no plans for Orion’s launch this year, emphasizing a focus on other technologies instead. Conflicting Reports Recent reports indicated that Orion might be OpenAI’s next major model, with expectations for a December launch and previews for trusted partners like Microsoft. Microsoft’s Anticipation Microsoft, a key collaborator with OpenAI, was expected to gain early access to Orion as soon as November, raising questions about the product timeline. Future Developments While Orion is on hold, OpenAI reassures users of upcoming releases of other innovative technologies. Read more at: TechCrunch

Introduction of SynthID Google has made its SynthID watermarking technology open source, allowing developers and businesses to embed imperceptible watermarks in AI-generated content. Purpose of the Toolkit This toolkit enables the AI industry to mark content as artificially generated, providing a means to detect deepfakes and other misleading AI content before it spreads. Watermarking Techniques SynthID is used across various media formats, including audio, video, and images, employing different techniques for each to ensure reliable detection. Methodology Explained A recent Nature paper details how SynthID embeds watermarks into text outputs from the Gemini AI model using a sampling algorithm that influences token selection during text generation. Limitations to Consider Despite its potential, experts note several limitations that may hinder the widespread adoption of AI watermarking as a standard practice in the industry. Read more at: Ars Technica