# README for Evaluation ## 🌟 Overview This script provides an evaluation pipeline for `MMMU`. While the provided code can run the benchmark, we recommend using [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) for testing this benchmark if you aim to align results with our technical report. The scores obtained using the code here will be approximately 2-3 points lower than those from VLMEvalKit. ## 🗂️ Data Preparation Before starting to download the data, please create the `InternVL/internvl_chat/data` folder. ### MMMU The evaluation script will automatically download the MMMU dataset from HuggingFace, and the cached path is `data/MMMU`. ## 🏃 Evaluation Execution > ⚠️ Note: For testing InternVL (1.5, 2.0, 2.5, and later versions), always enable `--dynamic` to perform dynamic resolution testing. To run the evaluation, execute the following command on an 8-GPU setup: ```shell torchrun --nproc_per_node=8 eval/mmmu/evaluate_mmmu.py --checkpoint ${CHECKPOINT} --dynamic ``` Alternatively, you can run the following simplified command: ```shell GPUS=8 sh evaluate.sh ${CHECKPOINT} mmmu-val --dynamic ``` ### Arguments The following arguments can be configured for the evaluation script: | Argument | Type | Default | Description | | ---------------- | ------ | ------------------- | ----------------------------------------------------------------------------------------------------------------- | | `--checkpoint` | `str` | `''` | Path to the model checkpoint. | | `--datasets` | `str` | `'MMMU_validation'` | Comma-separated list of datasets to evaluate. | | `--dynamic` | `flag` | `False` | Enables dynamic high resolution preprocessing. | | `--max-num` | `int` | `6` | Maximum tile number for dynamic high resolution. | | `--load-in-8bit` | `flag` | `False` | Loads the model weights in 8-bit precision. | | `--auto` | `flag` | `False` | Automatically splits a large model across 8 GPUs when needed, useful for models too large to fit on a single GPU. |