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Firoj Alam
Firoj Alam

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Benchmarking LLMs Made Easy with LLMeBench

๐Ÿ”น Are you evaluating Large Language Models (LLMs) for your NLP tasks?
๐Ÿ”น Do you need a flexible, scalable framework that supports multiple providers?

Look no furtherโ€”LLMeBench is here!

What is LLMeBench?

LLMeBench is an open-source benchmarking framework designed to help researchers and developers evaluate LLMs across different tasks, providers, and languages.

LLMeBench

With LLMeBench 1.1.0, weโ€™ve added:

โœ… Expanded modality support (text, vision, multimodal tasks)
โœ… More evaluation metrics for precise comparisons
โœ… Improved dataset integration for smoother benchmarking

๐Ÿ”— GitHub Repo โ†’ github.com/qcri/LLMeBench

๐Ÿ’ก Why Benchmarking LLMs is Important

The rapid rise of GPT-4, BLOOMZ, Falcon, and LLaMA has created a need for systematic performance evaluation. LLMs behave differently across tasks, datasets, and languages, making standardized benchmarking essential for:

๐Ÿ“Œ Model Comparison โ†’ Which LLM performs best for a specific task?
๐Ÿ“Œ Cost & Latency Analysis โ†’ Is an LLM efficient for real-world deployment?
๐Ÿ“Œ Fairness & Bias Detection โ†’ Does the model exhibit language-specific biases?

LLMeBench addresses these challenges with a structured benchmarking approach that supports various model providers like:
๐ŸŸข OpenAI (GPT models)
๐ŸŸข Hugging Face Inference API
๐ŸŸข Azure AI models
๐ŸŸข Models deployed through VLLM

Getting Started with LLMeBench

  1. Install LLMeBench

    pip install 'llmebench[fewshot]'

  2. Download the current assets:


python -m llmebench assets download

This will fetch assets and place them in the current working directory.

  1. Download one of the dataset, e.g. ArSAS.


python -m llmebench data download ArSAS

This will download the data to the current working directory inside the data folder.

  1. Evaluate!

For example, to evaluate the performance of a random baseline for Sentiment analysis on ArSAS dataset, you can run:


python -m llmebench --filter 'sentiment/ArSAS_Random*' assets/ results/

which uses the ArSAS_random "asset": a file that specifies the dataset, model and task to evaluate. Here, ArSAS_Random is the asset name referring to the ArSAS dataset name and the Random model, and assets/ar/sentiment_emotion_others/sentiment/ is the directory where the benchmarking asset for the sentiment analysis task on Arabic ArSAS dataset can be found. Results will be saved in a directory called results.

  1. View the Results

LLMeBench generates a performance report with:

๐Ÿ“Š Accuracy
โณ Response time
๐Ÿ“ˆ Task-specific metrics

๐ŸŽฏ Why Use LLMeBench?

โœ” Works with any NLP model & dataset
โœ” Supports multiple providers (OpenAI, HF, Azure, Petals)
โœ” Handles multimodal & multilingual benchmarking
โœ” Saves time & effort in evaluation

โญ Join the Community & Contribute

Weโ€™re excited to see researchers & developers using LLMeBench for their benchmarking needs! ๐Ÿš€

๐Ÿ”— Try LLMeBench today: github.com/qcri/LLMeBench
โญ If you find it useful, give us a star on GitHub!

๐Ÿ’ฌ Have feedback or feature requests? Open an issue or PR -- weโ€™d love to hear from you!

๐Ÿ’ก Whatโ€™s Next?

Weโ€™re constantly improving LLMeBench with new features & optimizations. Stay tuned for:
โœ… More task-specific benchmarking modules
โœ… Fine-grained evaluation for multilingual models
โœ… Support for additional model providers

๐Ÿ”ฅ If youโ€™re working with LLMs and benchmarking, weโ€™d love to hear how LLMeBench can help your workflow! Drop a comment below or connect with us on GitHub! ๐Ÿš€โœจ

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