About

With the plethora of large language models (LLMs) and chatbots being released week upon week, often with grandiose claims of their performance, it can be hard to filter out the genuine progress that is being made by the open-source community and which model is the current state of the art.

We wrote a release blog here to explain why we introduced this leaderboard!

Tasks

📈 We evaluate models on 6 key benchmarks using the Eleuther AI Language Model Evaluation Harness , a unified framework to test generative language models on a large number of different evaluation tasks.

For all these evaluations, a higher score is a better score. We chose these benchmarks as they test a variety of reasoning and general knowledge across a wide variety of fields in 0-shot and few-shot settings.

Results

You can find:

If a model’s name contains “Flagged”, this indicates it has been flagged by the community, and should probably be ignored! Clicking the link will redirect you to the discussion about the model.

Reproducibility

To reproduce our results, you can use our fork of lm_eval, as our PRs are not all merged in it at the moment.

git clone git@github.com:huggingface/lm-evaluation-harness.git
cd lm-evaluation-harness
git checkout adding_all_changess
pip install -e .[math,ifeval,sentencepiece]
lm-eval --model_args="pretrained=<your_model>,revision=<your_model_revision>,dtype=<model_dtype>" --tasks=leaderboard  --batch_size=auto --output_path=<output_path>

Attention: For instruction models add the --apply_chat_template and fewshot_as_multiturn option.

Note: You can expect results to vary slightly for different batch sizes because of padding.

Task Evaluations and Parameters

IFEval:

Big Bench Hard (BBH):

Math Challenges:

Generalized Purpose Question Answering (GPQA):

MuSR:

MMLU-PRO:

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