flexeval 0.17.1

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FlexEval

Flexible evaluation tool for language models. Easy to extend, highly customizable!

English | 日本語 |

With FlexEval, you can evaluate language models with:

• Zero/few-shot in-context learning tasks
• Open-ended text-generation benchmarks such as MT-Bench with automatic evaluation using GPT-4
• Log-probability-based multiple-choice tasks
• Computing perplexity of text data

For more use cases, see the documentation.

Key Features

• Flexibility: flexeval is flexible in terms of the evaluation setup and the language model to be evaluated.
• Modularity: The core components of flexeval are easily extensible and replaceable.
• Clarity: The results of evaluation are clear and all the details are saved.
• Reproducibility: flexeval should be reproducible, with the ability to save and load configurations and results.

Installation

pip install flexeval

Quick Start

The following minimal example evaluates the hugging face model sbintuitions/sarashina2.2-0.5b with the commonsense_qa task.

Run Command

flexeval_lm \
  --language_model HuggingFaceLM \
  --language_model.model "sbintuitions/sarashina2.2-0.5b" \
  --eval_setup "commonsense_qa" \
  --save_dir "results/commonsense_qa"

Output

...
2025-09-03 16:22:58.434 | INFO     | flexeval.core.evaluate_generation:evaluate_generation:92 - {'exact_match': 0.3185913185913186, 'finish_reason_ratio-stop': 1.0, 'avg_output_length': 9.095004095004095, 'max_output_length': 69, 'min_output_length': 2}
...

The results saved in --saved_dir contain:

• config.json: The configuration of the evaluation, which can be used to replicate the evaluation.
• metrics.json: The evaluation metrics.
• outputs.jsonl: The outputs of the language model that comes with instance-level metrics.

You can flexibly customize the evaluation by specifying command-line arguments or configuration files. Besides the Transformers model, you can also evaluate models via OpenAI ChatGPT and vLLM, and other models can be readily added!

Next Steps

• Run flexeval_presets to check the list of off-the-shelf presets in addition to commonsense_qa. You can find the details in the Preset Configs section.
• See Getting Started to check the tutorial examples for other kinds of tasks.
• See the Configuration Guide to set up your evaluation.