trtllm-eval#

About#

The trtllm-eval command provides developers with a unified entry point for accuracy evaluation. It shares the core evaluation logic with the accuracy test suite of TensorRT LLM.

trtllm-eval is built on the offline API – LLM API. Compared to the online trtllm-serve, the offline API provides clearer error messages and simplifies the debugging workflow.

The following tasks are currently supported:

Dataset	Task	Metric	Default ISL	Default OSL
CNN Dailymail	summarization	rouge	924	100
MMLU	QA; multiple choice	accuracy	4,094	2
GSM8K	QA; regex matching	accuracy	4,096	256
GPQA	QA; multiple choice	accuracy	32,768	4,096
JSON mode eval	structured generation	accuracy	1,024	512

Note

trtllm-eval originates from the TensorRT LLM accuracy test suite and serves as a lightweight utility for verifying and debugging accuracy. At this time, trtllm-eval is intended solely for development and is not recommended for production use.

Usage and Examples#

Some evaluation tasks (e.g., GSM8K and GPQA) depend on the lm_eval package. To run these tasks, you need to install lm_eval with:

pip install -r requirements-dev.txt

Alternatively, you can install the lm_eval version specified in requirements-dev.txt.

Here are some examples:

# Evaluate Llama-3.1-8B-Instruct on MMLU
trtllm-eval --model meta-llama/Llama-3.1-8B-Instruct mmlu

# Evaluate Llama-3.1-8B-Instruct on GSM8K
trtllm-eval --model meta-llama/Llama-3.1-8B-Instruct gsm8k

# Evaluate Llama-3.3-70B-Instruct on GPQA Diamond
trtllm-eval --model meta-llama/Llama-3.3-70B-Instruct gpqa_diamond

The --model argument accepts either a Hugging Face model ID or a local checkpoint path. By default, trtllm-eval runs the model with the PyTorch backend; you can pass --backend tensorrt to switch to the TensorRT backend.

Alternatively, the --model argument also accepts a local path to pre-built TensorRT engines. In this case, you should pass the Hugging Face tokenizer path to the --tokenizer argument.

For more details, see trtllm-eval --help and trtllm-eval <task> --help.

Syntax#

trtllm-eval#

trtllm-eval [OPTIONS] COMMAND [ARGS]...

Options

--model <model>#: Required model name | HF checkpoint path | TensorRT engine path

--tokenizer <tokenizer>#: Path | Name of the tokenizer.Specify this value only if using TensorRT engine as model.

--backend <backend>#

Set to ‘pytorch’ for pytorch path. Default is cpp path.

Options:: pytorch | tensorrt

--log_level <log_level>#

The logging level.

Options:: internal_error | error | warning | info | verbose | debug | trace

--max_beam_width <max_beam_width>#: Maximum number of beams for beam search decoding.

--max_batch_size <max_batch_size>#: Maximum number of requests that the engine can schedule.

--max_num_tokens <max_num_tokens>#: Maximum number of batched input tokens after padding is removed in each batch.

--max_seq_len <max_seq_len>#: Maximum total length of one request, including prompt and outputs. If unspecified, the value is deduced from the model config.

--tp_size <tp_size>#: Tensor parallelism size.

--pp_size <pp_size>#: Pipeline parallelism size.

--ep_size <ep_size>#: expert parallelism size

--gpus_per_node <gpus_per_node>#: Number of GPUs per node. Default to None, and it will be detected automatically.

--kv_cache_free_gpu_memory_fraction <kv_cache_free_gpu_memory_fraction>#: Free GPU memory fraction reserved for KV Cache, after allocating model weights and buffers.

--trust_remote_code#: Flag for HF transformers.

--extra_llm_api_options <extra_llm_api_options>#: Path to a YAML file that overwrites the parameters

cnn_dailymail#

trtllm-eval cnn_dailymail [OPTIONS]

Options

--dataset_path <dataset_path>#: The path to CNN Dailymail dataset. If unspecified, the dataset is downloaded from HF hub.

--num_samples <num_samples>#: Number of samples to run the evaluation; None means full dataset.

--random_seed <random_seed>#: Random seed for dataset processing.

--rouge_path <rouge_path>#: The path to rouge repository.If unspecified, the repository is downloaded from HF hub.

--apply_chat_template#: Whether to apply chat template.

--system_prompt <system_prompt>#: System prompt.

--max_input_length <max_input_length>#: Maximum prompt length.

--max_output_length <max_output_length>#: Maximum generation length.

gpqa_diamond#

trtllm-eval gpqa_diamond [OPTIONS]

Options

--dataset_path <dataset_path>#: The path to GPQA dataset. If unspecified, the dataset is downloaded from HF hub.

--num_samples <num_samples>#: Number of samples to run the evaluation; None means full dataset.

--random_seed <random_seed>#: Random seed for dataset processing.

--apply_chat_template#: Whether to apply chat template.

--system_prompt <system_prompt>#: System prompt.

--max_input_length <max_input_length>#: Maximum prompt length.

--max_output_length <max_output_length>#: Maximum generation length.

gpqa_extended#

trtllm-eval gpqa_extended [OPTIONS]

Options

--dataset_path <dataset_path>#: The path to GPQA dataset. If unspecified, the dataset is downloaded from HF hub.

--num_samples <num_samples>#: Number of samples to run the evaluation; None means full dataset.

--random_seed <random_seed>#: Random seed for dataset processing.

--apply_chat_template#: Whether to apply chat template.

--system_prompt <system_prompt>#: System prompt.

--max_input_length <max_input_length>#: Maximum prompt length.

--max_output_length <max_output_length>#: Maximum generation length.

gpqa_main#

trtllm-eval gpqa_main [OPTIONS]

Options

--dataset_path <dataset_path>#: The path to GPQA dataset. If unspecified, the dataset is downloaded from HF hub.

--num_samples <num_samples>#: Number of samples to run the evaluation; None means full dataset.

--random_seed <random_seed>#: Random seed for dataset processing.

--apply_chat_template#: Whether to apply chat template.

--system_prompt <system_prompt>#: System prompt.

--max_input_length <max_input_length>#: Maximum prompt length.

--max_output_length <max_output_length>#: Maximum generation length.

gsm8k#

trtllm-eval gsm8k [OPTIONS]

Options

--dataset_path <dataset_path>#: The path to GSM8K dataset. If unspecified, the dataset is downloaded from HF hub.

--num_samples <num_samples>#: Number of samples to run the evaluation; None means full dataset.

--random_seed <random_seed>#: Random seed for dataset processing.

--apply_chat_template#: Whether to apply chat template.

--system_prompt <system_prompt>#: System prompt.

--max_input_length <max_input_length>#: Maximum prompt length.

--max_output_length <max_output_length>#: Maximum generation length.

json_mode_eval#

trtllm-eval json_mode_eval [OPTIONS]

Options

--dataset_path <dataset_path>#: The path to JSON Mode Eval dataset. If unspecified, the dataset is downloaded from HF hub.

--num_samples <num_samples>#: Number of samples to run the evaluation; None means full dataset.

--random_seed <random_seed>#: Random seed for dataset processing.

--system_prompt <system_prompt>#: System prompt.

--max_input_length <max_input_length>#: Maximum prompt length.

--max_output_length <max_output_length>#: Maximum generation length.

mmlu#

trtllm-eval mmlu [OPTIONS]

Options

--dataset_path <dataset_path>#: The path to MMLU dataset. The commands to prepare the dataset: wget https://people.eecs.berkeley.edu/~hendrycks/data.tar && tar -xf data.tar. If unspecified, the dataset is downloaded automatically.

--num_samples <num_samples>#: Number of samples to run the evaluation; None means full dataset.

--num_fewshot <num_fewshot>#: Number of fewshot.

--random_seed <random_seed>#: Random seed for dataset processing.

--apply_chat_template#: Whether to apply chat template.

--system_prompt <system_prompt>#: System prompt.

--max_input_length <max_input_length>#: Maximum prompt length.

--max_output_length <max_output_length>#: Maximum generation length.

--check_accuracy#

--accuracy_threshold <accuracy_threshold>#