From a HuggingFace Model

The HuggingFace source lets you benchmark a model you have not deployed yet. You search the HuggingFace Hub from inside BenchGen, pick a model, and BenchGen deploys it for you and runs the evaluation. This is the fastest way to score a public model against an environment without adding it to your workspace first.

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Prerequisites

• A benchmark or environment to evaluate against, from the Environments Hub or a custom environment you uploaded.
• GPU capacity for BenchGen to deploy the model onto.
• For private or gated models, a HuggingFace access token.

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Steps

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1. Open a benchmark

Open the environment you want to evaluate against. The Overview tab describes what the benchmark measures and how it scores submissions. Click Evaluate in the top right, or open the Evaluate tab.

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2. Switch to the HuggingFace source

On the Evaluate tab, the model sources are grouped into tabs: Platform Models, Running, Trained, HuggingFace, and External API. Click HuggingFace.

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3. Search the Hub

Type a model name to search the HuggingFace Hub. Results show the repository, download and like counts, and the task tag (for example text-generation).

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4. Select and deploy the model

Click a result to select it. BenchGen deploys the model and confirms with Deployed & selected. The Advanced: model environment & parameters panel now reads auto-filled from selected model, so the model connection values are wired up for you. The environment variables the benchmark declares are filled from this deployed model. You only fill in the fields the benchmark owner exposed (here GK_SYSTEM_PROMPT and GK_MAX_QUESTIONS), and you can adjust the sampling parameters. See Environment variables for how these values are injected.

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5. Run the evaluation

Click Run Evaluation. BenchGen creates the run and opens a live view. While the model comes online, the status reads Deploying and the logs wait for output. The Evaluation details panel summarizes the run: status, model, environment, phase, submission archive, and the run ID.

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6. Review the results

When the run finishes, the status turns Completed and the Score Breakdown appears. The example below scored 92% (46 of 50 correct). The Detailed Results table lists each question with its correct answer (Doğru), the model’s answer (Model), and whether it matched (Sonuç). Download the prediction output, scoring output, or the full HTML/JSON breakdown from the Files panel. See Read results for a full walkthrough.

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Next steps

• Read results: interpret the score breakdown and detailed table.
• Environment variables: how run values reach the model.
• On a platform model: benchmark a model already on BenchGen.