In the evolving landscape of language models, Prometheus 2 stands out as a robust alternative for evaluating AI responses. It’s particularly effective for fine-grained evaluation of large language models (LLMs) using Reinforcement Learning from Human Feedback (RLHF). This guide will help you navigate the usage of Prometheus 2, detailing the setup, prompt formats, and troubleshooting methods.

Introduction to Prometheus 2

Prometheus 2, based on the Mistral-Instruct model, has been fine-tuned using a significant amount of feedback—100K for Feedback Collection and 200K for Preference Collection. This model uniquely employs weight merging, enhancing its ability to handle both absolute grading and relative grading, making it a versatile tool in the AI toolbox.

Setup and Installation

• Clone the Repository: Start by cloning the repository from GitHub.
• Install Requirements: Ensure that you have the necessary dependencies installed. Follow the instruction in the repository’s README file.
• Load the Model: Load Prometheus 2 using the provided functions within the library.

Using Prometheus 2

Prometheus 2 requires specific inputs based on whether you’re aiming for absolute grading or relative grading. Let’s break down the components you’ll need.

1. Absolute Grading (Direct Assessment)

For this method, you need the following four components:

• An instruction to guide the evaluation.
• A response that you will evaluate.
• A reference answer that serves as the ideal response, scoring a 5.
• A score rubric to define the scoring criteria.

Here’s how it works:

Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)

2. Relative Grading (Pairwise Ranking)

This approach compares two responses, requiring:

• An instruction.
• Response A and Response B for comparison.
• A reference answer.
• A score rubric.

Your output should look like this:

Feedback: (write a feedback for criteria) [RESULT] (A or B)

Understanding the Evaluation Process

Utilizing Prometheus 2 for evaluation is like conducting a fine art critique. Just as an art critic studies the nuances of brush strokes, colors, and themes to derive a deeper meaning in a painting, Prometheus 2 meticulously analyzes the responses according to specified rubrics to produce insightful feedback. Each assessment reflects the tailored standards set for performance, ensuring a high level of objectivity and clarity.

Troubleshooting

If you encounter issues while using Prometheus 2, consider the following troubleshooting tips:

• Ensure you have the correct dependencies installed as per the instructions in the README file.
• Double-check your prompt format. Ensure that you’re using the right structure for either absolute or relative grading.
• If the model doesn’t return valid feedback, verify the inputs—particularly the instruction and scoring rubric.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

Prometheus 2 is more than just a model; it’s a key to refined LLM evaluation, paving the way for improved AI interactions. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.