MMLU Pro
Evaluate your Large Language Model's (LLM) knowledge and reasoning across 57 diverse subjects using the MMLU benchmark. This Action Pack guides you through setting up and running MMLU to get a comprehensive assessment of your model's performance.
5 Steps
- 1
Install the Evaluation Harness: Install the `lm_eval` library, which provides an easy-to-use interface for running MMLU and other benchmarks. It's recommended to use a virtual environment.
- 2
Prepare Your LLM: Ensure your LLM is accessible. For Hugging Face models, you'll need the model name. For local models, ensure they are loaded correctly or provide the path. This pack assumes a Hugging Face model for the starter.
- 3
Run the MMLU Benchmark: Execute the `lm_eval` command, specifying the MMLU task. You can choose to run specific subjects or the full benchmark. The example uses a few-shot setting (5-shot) as is common for MMLU.
- 4
Analyze the Results: Review the output from `lm_eval`. It will provide accuracy scores for each MMLU subject and an overall average. Look for strengths and weaknesses across different domains.
- 5
Iterate and Improve: Use the MMLU scores to identify areas where your LLM underperforms. Fine-tune your model on relevant data or adjust its architecture to improve knowledge and reasoning in those specific subjects.
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