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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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Welcome to our community focused on hacking, cracking, and tech discussions. The content here is intended for educational purposes, but we must ensure a respectful, secure, and organized space. Please read and follow these rules: 1. Educational Intent Only The primary purpose of this forum is for educational discussions on hacking, cracking, and cybersecurity techniques. Use this space to share knowledge, learn, and develop skills. Any discussions must be framed as hypothetical or for research purposes only. No encouragement or incitement to use information for illegal purposes. You are responsible for ensuring that your use of this knowledge complies with your local laws. 2. No Real-World Attacks or Fraud Do not post, request, or share real-world exploits, illegal account access, credit card fraud, or identity theft in a way that directly encourages or facilitates crime. Discussions around vulnerabilities, cracking, and tools are allowed only in an educational context. No sharing of actual stolen data, credentials, or card details. 3. Respect Privacy Do not post, request, or share personal identifying information (e.g., real names, addresses, passwords) without explicit consent. No doxxing or revealing real-world identities, even in hypothetical discussions. Respect the anonymity of members and maintain their privacy. 4. Ethical Use of Tools When sharing tools or exploits, make clear that they are for educational use. Indicate any ethical considerations. Do not post or request malware, ransomware, or any other tool designed with the sole intent to cause harm (outside of educational malware analysis). Posting reverse-engineering tutorials, software bypass methods, and cracking tools is permitted only in a learning or research context. 5. No Real-Life Targeting Do not post or request exploits targeting specific organizations, individuals, or websites with the intent to cause harm. Keep discussions theoretical or related to simulated environments for learning purposes (e.g., Capture the Flag events, private lab setups). 6. No Spam or Unsolicited Promotion Avoid irrelevant promotions, spam, or unsolicited advertisements, especially unrelated to the forum's core topics. Self-promotion of blogs, services, or tools is allowed in designated areas, provided it contributes value to the community. 7. Stay On-Topic Keep discussions within the relevant categories. Off-topic discussions should be limited to the appropriate sections (e.g., Off-Topic Lounge). Use descriptive titles for posts and tag content appropriately to help others navigate. 8. Report and Respect Moderators If you see content that violates these guidelines, report it to the moderators. Moderators will handle all violations, and their decisions are final. Respect their role in maintaining the forum's structure. Any disputes with moderation should be addressed privately. 9. No Harmful Sharing Avoid posting harmful content such as explicit step-by-step guides for criminal activities (e.g., bypassing systems in real-time, phishing). Instead, keep it theoretical. No real-world distribution of cracked software, stolen credentials, or access methods that can lead to direct misuse. 10. Legal Disclaimer All content and discussions on this forum are intended for educational and research purposes only. Forum administrators, moderators, and members are not responsible for any actions taken as a result of the information shared. Users are expected to understand and comply with the laws in their respective countries. Remember Your contributions should aim to educate and foster discussions on hacking and cybersecurity practices in a responsible way. We do not condone illegal activities, and members who directly facilitate or promote illegal use will be removed from the forum