deepmoney 34b 200k base

Introduction

The DEEPMONEY-34B-200K-BASE model is a sophisticated financial language model designed to enhance investment decision-making processes. It is part of the "Greed" series in the Seven Deadly Sins models, aiming to address the limitations of traditional financial models by incorporating both qualitative and quantitative analyses. This model focuses on providing a comprehensive understanding of financial markets through high-quality training data and advanced processing techniques.

Architecture

The DEEPMONEY model is built on a large-scale architecture, utilizing the Yi-34b framework known for its long-context capabilities. This enables the model to process extensive amounts of information, crucial for generating detailed financial reports and analyses. The model integrates various components, including information collectors, target judges, and data extractors, to facilitate a holistic analysis pipeline.

Training

The training process for DEEPMONEY involved a combination of high-quality textbooks and proprietary research reports from 2019 to December 2023. These data sources were acquired through various means and include insights from traditional brokers and professional research institutions. The training emphasized both qualitative judgments and quantitative analyses, leveraging multi-modal models like cog-agent and emu2 for effective data extraction from various formats such as graphs and tables.

Guide: Running Locally

To run the DEEPMONEY model locally, follow these basic steps:

1. Installation: Ensure you have Python and necessary libraries installed. You can use package managers like pip to install dependencies.
2. Model Download: Access the model files from the Hugging Face repository and download them to your local environment.
3. Setup Environment: Configure your environment with the required hardware and software specifications. Consider using virtual environments for dependency management.
4. Execution: Load the model in your preferred ML framework (e.g., PyTorch) and execute your desired tasks.
5. Optimization: Depending on your task, optimize the model's performance by adjusting parameters and utilizing efficient data processing techniques.

Cloud GPU Suggestions: For optimal performance, especially for large-scale data processing, consider using cloud-based GPU services such as AWS EC2 instances with NVIDIA GPUs, Google Cloud's Tensor Processing Units (TPUs), or Azure's GPU virtual machines.

License

The DEEPMONEY-34B-200K-BASE model is released under the Apache 2.0 license. This allows for both personal and commercial use, ensuring flexibility in how the model can be deployed and integrated into various applications.