hugging-face-evaluation

Importing benchmark scores from Artificial Analysis allows you to fetch fresh external API data. You can automatically update your Hugging Face model card leaderboard scores by extracting these benchmark results and submitting them as a pull request.

You can execute custom model evaluations using vLLM or lighteval directly within the workflow. Running these evaluations generates structured benchmark results that integrate with the model-index for seamless leaderboard compatibility.

Yes, managing Hugging Face model card evaluations supports Papers with Code specifications. This integration structures your benchmark results to maintain compatibility with leaderboard standards and external evaluation platforms.

The best way to update Hugging Face model card leaderboard scores is by extracting results from the README or fetching benchmarks via an external API. You can then submit the structured evaluation data as a pull request to update the model card.

Yes, you can use lighteval and vLLM for benchmarking Hugging Face models. This Skill executes custom evaluations using these frameworks and integrates the resulting structured data directly into the model card for leaderboard indexing.