huggingface-community-evals

Run local evaluations on Hugging Face Hub models by using inspect-ai and lighteval. This Skill automates performance assessment on local hardware to help you compare different models for your projects.

For local model evaluation, you can use vLLM, Hugging Face Transformers, or accelerate as inference backends. These options allow you to select the best backend for your local GPU resources.

Yes, you can compare Hugging Face models locally on your GPU. This Skill supports local GPU evaluation, allowing you to assess model performance and make decisions based on local results.

To evaluate Hugging Face models locally, you need inspect-ai, inspect-evals, vLLM, Hugging Face Transformers, or accelerate installed. These frameworks provide the execution environment for local inference.

The best way to assess Hugging Face Hub model performance is running local evaluations using inspect-ai. You can evaluate models like meta-llama/Llama-3.2-1B on tasks such as mmlu to compare results.

Yes, inspect-ai works with vLLM for local Hugging Face model evaluation. You can specify vLLM as the backend to run inference and evaluate model performance on supported tasks.