Use SGLang as backend

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

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

sglang is able to distribute the model across multiple GPUs using data parallelism and tensor parallelism. You can choose the parallelism method by setting in the model_args.

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

lighteval sglang \
    "model_name=HuggingFaceH4/zephyr-7b-beta,dtype=float16,tp_size=4" \
    "leaderboard|truthfulqa:mc|0|0"

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

lighteval sglang \
    "model_name=HuggingFaceH4/zephyr-7b-beta,dtype=float16,dp_size=4" \
    "leaderboard|truthfulqa:mc|0|0"

Use a config file

For more advanced configurations, you can use a config file for the model. An example of a config file is shown below and can be found at examples/model_configs/sglang_model_config.yaml.

lighteval sglang \
    "examples/model_configs/sglang_model_config.yaml" \
    "leaderboard|truthfulqa:mc|0|0"

Documentation for the config file of sglang can be found here

model_parameters:
    model_name: "HuggingFaceTB/SmolLM-1.7B-Instruct"
    dtype: "auto"
    tp_size: 1
    dp_size: 1
    context_length: null
    random_seed: 1
    trust_remote_code: False
    use_chat_template: False
    device: "cuda"
    skip_tokenizer_init: False
    kv_cache_dtype: "auto"
    add_special_tokens: True
    pairwise_tokenization: False
    sampling_backend: null
    attention_backend: null
    mem_fraction_static: 0.8
    chunked_prefill_size: 4096
    generation_parameters:
      max_new_tokens: 1024
      min_new_tokens: 0
      temperature: 1.0
      top_k: 50
      min_p: 0.0
      top_p: 1.0
      presence_penalty: 0.0
      repetition_penalty: 1.0
      frequency_penalty: 0.0

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

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