# README for Evaluation ## 🌟 Overview This script provides an evaluation pipeline for `Tiny-LVLM-eHub`. ## 🗂️ Data Preparation Before starting to download the data, please create the `InternVL/internvl_chat/data` folder. ### Tiny-LVLM-eHub Follow the instructions below to prepare the data: ```shell # Step 1: Create the data directory mkdir -p data/tiny_lvlm && cd data/tiny_lvlm # Step 2: Download the dataset wget https://huggingface.co/OpenGVLab/InternVL/resolve/main/updated_datasets.zip unzip updated_datasets.zip cd ../.. ``` After preparation is complete, the directory structure is: ```shell data/tiny_lvlm └── updated_datasets ``` ## 🏃 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/tiny_lvlm/evaluate_lvlm.py --checkpoint ${CHECKPOINT} --dynamic ``` Alternatively, you can run the following simplified command: ```shell GPUS=8 sh evaluate.sh ${CHECKPOINT} tiny_lvlm --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` | `'updated_datasets'` | 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. |