Awesome
RBDash v1.5
install
- Clone this repository and navigate to RBDash folder
git clone https://github.com/rbdash.git
cd RBDash
- Install Package
conda create -n rbdash python=3.10 -y
conda activate rbdash
pip install --upgrade pip
pip install -e .
- Install additional packages for training cases
pip install ninja
pip install flash-attn --no-build-isolation
- or install the specific version of flash_attn from the .whl file: If you have already downloaded the flash_attn wheel file, for example, flash_attn-2.5.8+cu122torch2.1cxx11abiFALSE-cp310-cp310-linux_x86_64.whl, you can install it with the following command:
pip install flash_attn-2.5.8+cu122torch2.1cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
Upgrade to latest code base
git pull
pip uninstall transformers
pip install -e .
Pretrained Weights
We recommend users to download the pretrained weights from the following link OpenCLIP-ConvNeXt-L, InternViT-6B-448px-V1-5,and put them in model_zoo following Structure.
Structure
RBDASH
├── rbdash
├── scripts
├── model_zoo
│ ├── OpenAI
│ │ ├── InternViT-6B-448px-V1-5
│ │ ├── openclip-convnext-large-d-320-laion2B-s29B-b131K-ft-soup
│ │ ├── ...
Model Zoo
Evaluation
In RBDash, we evaluate models on MME.
MME
- Download the data following the official instructions here.
- Downloaded images to ./rbdash-Eval/MME/MME_Benchmark_release_version.
- Downloaded and put the weights to ./models/RBDash-v1.5
- inference and evaluate.
bash scripts/rbdash/eval/mme.sh