

Prometheus Eval
Overview :
Prometheus-Eval is an open-source toolkit designed to assess the performance of large language models (LLMs) in generation tasks. It provides a straightforward interface for evaluating instructions and responses using the Prometheus model. The Prometheus 2 model supports direct evaluation (absolute scoring) and paired ranking (relative scoring), which can simulate human judgment and proprietary language model-based evaluation, addressing issues of fairness, control, and affordability.
Target Users :
["Researchers and developers: For evaluating and optimizing their own language models","Educational institutions: As a teaching tool to help students understand the evaluation process of language models","Enterprises: Building internal evaluation processes without relying on proprietary models, protecting data privacy"]
Use Cases
Evaluate the performance of a language model in sentiment analysis tasks
Compare the advantages and disadvantages of two different models in text generation tasks
Serve as a benchmark for developing new language models
Features
Absolute scoring: Outputs a score between 1 and 5 based on the given instructions, reference answers, and scoring criteria
Relative scoring: Evaluates two responses based on the given instructions and scoring criteria, outputting 'A' or 'B' to indicate the better response
Supports direct download of model weights from Huggingface Hub
Provides a Python package, prometheus-eval, to simplify the evaluation process
Includes scripts for training Prometheus models or fine-tuning on custom datasets
Supplies evaluation datasets for training and assessing Prometheus models
Supports operation on consumer-grade GPUs, reducing resource requirements
How to Use
Step 1: Install the Prometheus-Eval Python package
Step 2: Prepare the instructions, responses, and scoring criteria required for evaluation
Step 3: Evaluate using the absolute scoring or relative scoring method
Step 4: Analyze the model's performance based on the output scores or rankings
Step 5: Adjust and optimize the language model based on the evaluation results
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