
Mercury
Code Efficiency Benchmark
Stars: 89

Mercury is a code efficiency benchmark designed for code synthesis tasks. It includes 1,889 programming tasks of varying difficulty levels and provides test case generators for comprehensive evaluation. The benchmark aims to assess the efficiency of large language models in generating code solutions.
README:
- Welcome to Mercury!
- Mercury is the first code efficiency benchmark designed for code synthesis tasks.
- It consists of 1,889 programming tasks covering diverse difficulty levels, along with test case generators that produce unlimited cases for comprehensive evaluation.
[October 8, 2024] Mercury has been accepted to NeurIPS 2024 🌟
[September 20, 2024] We release a way bigger dataset Venus, which supports more languages. It also provides Memory measurement other than Time.
[July 10, 2024] We are building Code Arena now for more efficient Code LLMs evaluation!
[June 24, 2024] We are currently working on the Multilingual Mercury 🚧
[May 26, 2024] Mercury is now available on BigCode 🌟
We publish and maintain our datasets at Mercury@HF
# Option 1 (with BigCode):
# See https://github.com/bigcode-project/bigcode-evaluation-harness/tree/main/docs#mercury
accelerate launch --main_process_port 30003 main.py \
--model bigcode/starcoder2-7b \
--load_in_4bit \
--max_length_generation 2048 \
--tasks mercury \
--n_samples 5 \
--temperature 0.2 \
--batch_size 5 \
--allow_code_execution \
--save_generations \
--metric_output_path starcoder2-7b-mercury-result.json
# Option 2 (this library):
import os
os.environ["OPENAI_API_KEY"] = 'YOUR_OPENAI_KEY'
# Instantiate evaluator with model_name
# Set do_generate to True if you are going to load the specific language model during evaluator initialization.
from src import evaluator as Evaluator
evaluator = Evaluator.DistributeWiseEvaluator(model_name_or_path='openai/gpt-3.5-turbo-1106', do_generate=True)
# Generate code samples
evaluator.generate(num_samples_per_task=1)
# Evaluate code samples using the Mercury benchmark
evaluator.evaluate(num_samples_per_task=1)
@inproceedings{du2024mercury,
title={Mercury: A code efficiency benchmark for code large language models},
author={Du, Mingzhe and Luu, Anh Tuan and Ji, Bin and Liu, Qian and Ng, See-Kiong},
booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2024}
}
Should you have any questions regarding this paper, please feel free to email us ([email protected]). Thank you for your attention!
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