Reproducing Leaderboard Benchmarks: Evaluate Your LLM Like Hugging Face
In this video, we dive into LLM benchmarking and show how Hugging Face evaluates large language models on the Open LLM Leaderboard. You’ll learn what these scores actually mean, how they are calculated, and how you can reproduce them on your own models.
We walk through the evaluation setup, explain how different datasets are used, and demonstrate how to run official benchmark tasks on your own LLM using an open-source evaluation framework. You’ll also see how to inspect results, verify accuracy manually, and understand what these metrics really measure.
You’ll learn how to:
* Understand how…
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Chapters (9)
What LLM benchmarking is and why it matters
0:45
How Hugging Face leaderboard scores are calculated
1:23
MCQ vs generation-based evaluation datasets
2:16
Instruction-following benchmarks explained
3:07
Running official Hugging Face evaluation code
4:42
Reproducing leaderboard-style results
5:55
Understanding strict match vs flexible match
7:00
Inspecting samples and verifying accuracy
9:03
Comparing your model to the leaderboard
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