Use VLLM as backend

Lighteval allows you to use vllm as backend allowing great speedups. To use, simply change the model_args to reflect the arguments you want to pass to vllm.

lighteval vllm \
    "pretrained=HuggingFaceH4/zephyr-7b-beta,dtype=float16" \
    "leaderboard|truthfulqa:mc|0|0"

vllm is able to distribute the model across multiple GPUs using data parallelism, pipeline parallelism or tensor parallelism. You can choose the parallelism method by setting in the the model_args.

For example if you have 4 GPUs you can split it across using tensor_parallelism:

export VLLM_WORKER_MULTIPROC_METHOD=spawn && lighteval vllm \
    "pretrained=HuggingFaceH4/zephyr-7b-beta,dtype=float16,tensor_parallel_size=4" \
    "leaderboard|truthfulqa:mc|0|0"

Or, if your model fits on a single GPU, you can use data_parallelism to speed up the evaluation:

lighteval vllm \
    "pretrained=HuggingFaceH4/zephyr-7b-beta,dtype=float16,data_parallel_size=4" \
    "leaderboard|truthfulqa:mc|0|0"

Available arguments for vllm can be found in the VLLMModelConfig:

In the case of OOM issues, you might need to reduce the context size of the model as well as reduce the gpu_memory_utilisation parameter.

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