Welcome to this guide where we take a deep dive into how to leverage Prometheus 2 for evaluating language models with precision. This powerful alternative to GPT-4 shines when conducting meticulous evaluations, especially with Reinforcement Learning from Human Feedback (RLHF). Let’s explore the steps you need to follow to implement this model seamlessly.

Introduction to Prometheus 2

Prometheus 2 is a sophisticated language model built on [Mistral-Instruct](https://huggingface.comistralaiMistral-7B-Instruct-v0.2), fine-tuned using extensive feedback from both the Feedback Collection and Preference Collection. It’s designed to enhance the evaluation metrics and can perform absolute (direct assessment) and relative (pairwise ranking) grading.

Getting Started with Prometheus 2

Model Details

• Model Type: Language model
• Language: English
• License: Apache 2.0
• Checkpoints: Explore all Prometheus checkpoints for various sizes.

Preparing Your Evaluation

Prompt Format

To effectively use Prometheus 2, you will need to format your prompts correctly. There are two distinct methods based on the evaluation type: absolute grading and relative grading.

Absolute Grading (Direct Assessment)

The absolute grading approach involves evaluating a single response against a rubric. Picture this as a teacher grading an essay using a predefined scale from 1 (poor) to 5 (excellent). In this scenario:

• You present the model with an instruction, a response, a reference answer, and a scoring rubric.
• For example, if the student submitted an essay, the teacher (the model) provides detailed feedback based strictly on the criteria in the rubric, scoring accordingly.

Prompt Template Example:

Instruction: orig_instruction
Response to evaluate: orig_response
Reference Answer (Score 5): orig_reference_answer
Score Rubrics: [orig_criteria]

Relative Grading (Pairwise Ranking)

In this method, you have two responses to compare, akin to a judge in a cooking show deciding which dish looks and tastes better. Here’s what you need:

• Both responses will be evaluated based on the same criteria, determining which one stands out.
• The model provides feedback discussing the strengths and weaknesses of each response, ultimately championing one as superior.

Prompt Template Example:

Instruction: orig_instruction
Response A: orig_response_A
Response B: orig_response_B
Reference Answer: orig_reference_answer
Score Rubric: orig_criteria

Troubleshooting Tips

While using Prometheus 2, you may encounter a few hiccups. Here are some troubleshooting ideas:

• Incorrect Prompt Structure: Ensure all components of your prompts are filled and formatted properly. A missing element may lead to unexpected behaviors.
• Feedback Unclear: If the feedback generated lacks clarity, consider reviewing and refining the rubric. The model thrives on clear definitions!
• Performance Issues: If the model operates sluggishly, check your system requirements and dependencies to ensure compatibility.

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

Final Thoughts

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.

Now, you’re ready to harness the power of Prometheus 2 for precise evaluations of language models. Happy coding!