

Fin R1
Overview :
Fin-R1 is a large language model designed specifically for the financial field, aimed at enhancing financial reasoning capabilities. Jointly developed by Shanghai University of Finance and Economics and Caiyue Xingchen, it is fine-tuned and reinforced learning based on Qwen2.5-7B-Instruct, possessing efficient financial reasoning capabilities and is suitable for core financial scenarios such as banking and securities. This model is free and open-source, facilitating user adoption and improvement.
Target Users :
This product is suitable for financial professionals, investors, and financial institutions, as it can effectively improve the accuracy and efficiency of financial decision-making and meet the needs of high-quality financial reasoning.
Use Cases
Bank Risk Assessment System: Perform credit risk assessment using the Fin-R1 model.
Securities Market Analysis Tool: Use the model to analyze stock market trends.
Compliance Audit Assistant: Help enterprises establish a compliance management system and conduct regular checks.
Features
Financial Code Generation: Supports the generation of programming code for various financial models and algorithms.
Financial Calculation: Performs quantitative analysis and calculation of complex financial problems.
English Financial Calculation: Supports building and writing financial models using English.
Financial Security and Compliance: Helps enterprises ensure that business operations comply with relevant regulations.
Intelligent Risk Control: Uses AI technology to identify and manage financial risks, improving decision-making efficiency.
ESG Analysis: Assesses a company's sustainability and promotes the fulfillment of social responsibilities.
Multi-round Interaction Capability: Supports multi-round conversational interaction in the financial field.
High-Quality Data Support: Improves data quality through refined data distillation and screening.
How to Use
Access the GitHub page to download the Fin-R1 model.
Configure the environment and dependent libraries according to the documentation.
Select the required financial dataset to fine-tune the model.
Use the fine-tuned model for inference on specific financial tasks.
Adjust model parameters and input data as needed for multi-round interaction.
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