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RBDash v1.2

install

  1. Clone this repository and navigate to RBDash folder
git clone https://github.com/rbdash.git
cd RBDash
  1. Install Package
conda create -n rbdash python=3.10 -y
conda activate rbdash
pip install --upgrade pip
pip install -e .
pip install ninja
pip install flash-attn --no-build-isolation
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

RBDash-v1.2-72b

Evaluation

In RBDash, we evaluate models on MME.

MME

  1. Download the data following the official instructions here.
  2. Downloaded images to ./rbdash-Eval/MME/MME_Benchmark_release_version.
  3. Downloaded and put the weights to ./models/RBDash-v1.2-72b
  4. inference and evaluate.
bash scripts/rbdash/eval/mme.sh