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Quick Summary

LLM benchmarking provides a standardized way to quantify LLM performances across a range of different tasks. deepeval offers several state-of-the-art, research-backed benchmarks for you to quickly evaluate ANY custom LLM of your choice. These benchmarks include:

  • BIG-Bench Hard
  • HellaSwag
  • MMLU (Massive Multitask Language Understanding)

To benchmark your LLM, you will need to wrap your LLM implementation (which could be anything such as a simple API call to OpenAI, or a Hugging Face transformers model) within deepeval's DeepEvalBaseLLM class. Visit the custom models section for a detailed guide on how to create a custom model object.


In deepeval, anyone can benchmark any LLM of their choice in just a few lines of code.

What are LLM Benchmarks?

LLM benchmarks are a set of standardized tests designed to evaluate the performance of an LLM on various skills, such as reasoning and comprehension. A benchmark is made up of:

  • one or more tasks, where each task is its own evaluation dataset with target labels (or expected_outputs)
  • a scorer, to determine whether predictions from your LLM is correct or not (by using target labels as reference)
  • various prompting techniques, which can be either involve few-shot learning and/or CoTs prompting

The LLM to be evaluated will generate "predictions" for each tasks in a benchmark aided by the outlined prompting techniques, while the scorer will score these predictions by using the target labels as reference. There is no standard way of scoring across different benchmarks, but most simply uses the exact match scorer for evaluation.


A target label in a benchmark dataset is simply the expected_output in deepeval terms.

Benchmarking Your LLM

Below is an example of how to evaluate a Mistral 7B model (exposed through Hugging Face's transformers library) against the MMLU benchmark.

Start by creating a custom model by inheriting the DeepEvalBaseLLM class (visit the custom models section for a full guide on how to create a custom model):

from transformers import AutoModelForCausalLM, AutoTokenizer
from deepeval.models.base_model import DeepEvalBaseLLM

class Mistral7B(DeepEvalBaseLLM):
def __init__(
self.model = model
self.tokenizer = tokenizer

def load_model(self):
return self.model

def generate(self, prompt: str) -> str:
model = self.load_model()

device = "cuda" # the device to load the model onto

model_inputs = self.tokenizer([prompt], return_tensors="pt").to(device)

generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
return self.tokenizer.batch_decode(generated_ids)[0]

async def a_generate(self, prompt: str) -> str:
return self.generate(prompt)

def get_model_name(self):
return "Mistral 7B"

model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")

mistral_7b = Mistral7B(model=model, tokenizer=tokenizer)
print(mistral_7b("Write me a joke"))

Next, define a MMLU benchmark using the MMLU class:

from deepeval.benchmarks.mmlu import MMLU

benchmark = MMLU()

Lastly, call the evaluate() method to benchmark your custom LLM:


results = benchmark.evaluate(model=mistral_7b)
print("Overall Score: ", results)

Congraulations! You can now evaluate any custom LLM of your choice on all LLM benchmarks offered by deepeval.


All benchmarks offered by deepeval follows the implementation of the original research papers.

After running an evaluation, you can access the results in multiple ways to analyze the performance of your model. This includes the overall score, task-specific scores, and details about each prediction.

Overall Score

The overall_score, which represents your model's performance across all specified tasks, can be accessed through the overall_score attribute:


print("Overall Score:", benchmark.overall_score)

Task Scores

Individual task scores can be accessed through the task_scores attribute:


print("Task-specific Scores: ", benchmark.task_scores)

The task_scores attribute outputs a pandas DataFrame containing information about scores achieved in various tasks. Below is an example DataFrame:


Prediction Details

You can also access a comprehensive breakdown of your model's predictions across different tasks through the predictions attribute:


print("Detailed Predictions: ", benchmark.predictions)

The benchmark.predictions attribute also yields a pandas DataFrame containing detailed information about predictions made by the model. Below is an example DataFrame:

high_school_computer_scienceIn Python 3, which of the following function convert a string to an int in python?A0
high_school_computer_scienceLet x = 1. What is x << 3 in Python 3?B1

Configurating LLM Benchmarks

All benchmarks are configurable in one way or another, and deepeval offers an easy inferface to do so.


You'll notice although tasks and prompting techniques are configurable, scorers are not. This is because the type of scorer is an universal standard within any LLM benchmark.


A task for an LLM benchmark is a challenge or problem is designed to assess an LLM's capabilities on a specific area of focus. For example, you can specify which subset of the the MMLU benchmark to evaluate your LLM on by providing a list of MMLUTASK:

from deepeval.benchmarks import MMLU
from deepeval.benchmarks.task import MMLUTask

benchmark = MMLU(tasks=tasks)

In this example, we're only evaluating our Mistral 7B model on the MMLU HIGH_SCHOOL_COMPUTER_SCIENCE and ASTRONOMY tasks.


Each benchmark is associated with a unique Task enum which can be found on each benchmark's individual documentation pages. These tasks are 100% drawn from the original research papers for each respective benchmark, and maps one-to-one to the benchmark datasets available on Hugging Face.

By default, deepeval will evaluate your LLM on all available tasks for a particular benchmark.

Few-Shot Learning

Few-shot learning, also known as in-context learning, is a prompting technique that involves supplying your LLM a few examples as part of the prompt template to help its generation. These examples can help guide accuracy or behavior. The number of examples to provide, can be specified in the n_shots parameter:

from deepeval.benchmarks import HellaSwag

benchmark = HellaSwag(n_shots=3)

Each benchmark has a range of allowed n_shots values. deepeval handles all the logic with respect to the n_shots value according to the original research papers for each respective benchmark.

CoTs Prompting

Chain of thought prompting is an approach where the model is prompted to articulate its reasoning process to arrive at an answer. This usually results in an increase in prediction accuracy.

from deepeval.benchmarks import BigBenchHard

benchmark = BigBenchHard(enable_cot=True)

Not all benchmarks offers CoTs as a prompting technique, but the original paper for BIG-Bench Hard found major improvements when using CoTs prompting during benchmarking.