DeepHermes Financial Fundamentals Prediction Specialist - Atropos RL

Model Overview

The DeepHermes Financial Fundamentals Prediction Specialist - Atropos RL is an experimental model artifact, fine-tuned by Nous Research using our new open source LLM RL Gym, Atropos. This model specifically aims to enhance the accuracy of financial fundamentals predictions through reasoning-intensive reinforcement learning techniques.

Note: This model is experimental and is not intended as a general-purpose state-of-the-art solution.

Atropos Open Source Framework

Atropos is Nous Research's open-source reinforcement learning environment stack, engineered to optimize diverse LLM capabilities through structured RL methodologies. Contributions and active engagement from the community are highly encouraged:

🔗 Atropos GitHub Repository

Evaluation Results

Training and evaluation focused on improving financial fundamentals direction prediction accuracy:

Key Features

• Financial Fundamentals Prediction: Enhanced capability to predict market direction using RL during intensive reasoning tasks.
• Built with Atropos RL Environments: Built with the open-source Atropos reinforcement learning stack.
• Reasoning-Enhanced Predictions: Specifically optimized for scenarios involving deep analytical reasoning in financial contexts.

Usage

The model is optimized for reasoning-intensive financial analysis tasks and supports:

• Deep Reasoning Mode given a company's context (previous quarter's financials data) to predict the next future quarter's fundamental metric direction)

Community and Support

We welcome contributions, suggestions, and issues through our community channels:

• Atropos Issues

How to Cite

@misc{
      title={DeepHermes Financial Fundamentals Prediction Specialist - Atropos RL},
      author={Teknium and Dakota Mahan and Roger Jin and Chen Guang and Jai Suphavadeeprasit and Jeffrey Quesnelle},
      year={2025},
      url={https://huggingface.co/NousResearch/DeepHermes-Financial-Fundamentals-Prediction-Specialist-Atropos-RL}
}